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+1
+00:00:00,840 --> 00:00:05,920
+okay so uh let's get started um today
+
+2
+00:00:04,200 --> 00:00:08,000
+I'm going to be talking about learning
+
+3
+00:00:05,920 --> 00:00:09,480
+from Human feedback I wrote
+
+4
+00:00:08,000 --> 00:00:12,160
+reinforcement learning from Human
+
+5
+00:00:09,480 --> 00:00:14,519
+feedback because that's what um you know
+
+6
+00:00:12,160 --> 00:00:15,759
+a lot of people talk about nowadays but
+
+7
+00:00:14,519 --> 00:00:18,880
+actually there's other methods of
+
+8
+00:00:15,759 --> 00:00:21,840
+learning from Human feedback so first
+
+9
+00:00:18,880 --> 00:00:24,760
+I'm going to be talking about the ways
+
+10
+00:00:21,840 --> 00:00:27,920
+we can get uh human feedback for the
+
+11
+00:00:24,760 --> 00:00:31,039
+generations of models and mostly focus
+
+12
+00:00:27,920 --> 00:00:32,960
+on generation tasks because is um
+
+13
+00:00:31,039 --> 00:00:35,800
+generation tasks are harder than like
+
+14
+00:00:32,960 --> 00:00:38,559
+classification tasks that we uh we deal
+
+15
+00:00:35,800 --> 00:00:40,000
+with normally so I'll spend a fair
+
+16
+00:00:38,559 --> 00:00:42,239
+amount of time talking about how we do
+
+17
+00:00:40,000 --> 00:00:45,760
+that and then after I talk about how we
+
+18
+00:00:42,239 --> 00:00:48,360
+do that we'll move into um how we
+
+19
+00:00:45,760 --> 00:00:51,160
+actually learn from that
+
+20
+00:00:48,360 --> 00:00:53,399
+signal so normally what we've done up
+
+21
+00:00:51,160 --> 00:00:56,399
+until this point is maximum likelihood
+
+22
+00:00:53,399 --> 00:00:58,199
+training uh this is just an overview
+
+23
+00:00:56,399 --> 00:00:59,559
+slide so we what we want to do is we
+
+24
+00:00:58,199 --> 00:01:00,760
+want to maximize the likelihood of
+
+25
+00:00:59,559 --> 00:01:03,280
+predicting the next word and the
+
+26
+00:01:00,760 --> 00:01:05,960
+reference given the previous words uh
+
+27
+00:01:03,280 --> 00:01:08,119
+which gives us the loss of the output
+
+28
+00:01:05,960 --> 00:01:09,799
+given the input uh where you know the
+
+29
+00:01:08,119 --> 00:01:13,960
+input can be the prompt the output can
+
+30
+00:01:09,799 --> 00:01:16,080
+be the answer to uh the output but
+
+31
+00:01:13,960 --> 00:01:18,360
+there's uh lots of problems with
+
+32
+00:01:16,080 --> 00:01:20,439
+learning from Maximum likelihood and I'm
+
+33
+00:01:18,360 --> 00:01:22,079
+going to give three examples here I
+
+34
+00:01:20,439 --> 00:01:24,159
+think all of these are actually real
+
+35
+00:01:22,079 --> 00:01:26,880
+problems uh that we need to be worried
+
+36
+00:01:24,159 --> 00:01:30,240
+about so the first one is that some
+
+37
+00:01:26,880 --> 00:01:32,439
+mistakes are worse than others so um in
+
+38
+00:01:30,240 --> 00:01:33,560
+the end we want good outputs and some
+
+39
+00:01:32,439 --> 00:01:36,520
+mistaken
+
+40
+00:01:33,560 --> 00:01:38,200
+predictions uh can be a bigger problem
+
+41
+00:01:36,520 --> 00:01:42,680
+for the output being
+
+42
+00:01:38,200 --> 00:01:46,000
+good so to give an example uh let's say
+
+43
+00:01:42,680 --> 00:01:47,600
+what we actually wanted from like a
+
+44
+00:01:46,000 --> 00:01:49,320
+speech recognition system or a
+
+45
+00:01:47,600 --> 00:01:54,040
+translation system or something like
+
+46
+00:01:49,320 --> 00:01:54,040
+that is uh please send this package to
+
+47
+00:01:54,280 --> 00:01:58,920
+Pittsburgh if I write please send a
+
+48
+00:01:56,880 --> 00:02:01,560
+package to Pittsburgh then this is not a
+
+49
+00:01:58,920 --> 00:02:03,560
+huge problem
+
+50
+00:02:01,560 --> 00:02:06,479
+if I write uh please send this package
+
+51
+00:02:03,560 --> 00:02:07,719
+to Tokyo then that might be a big
+
+52
+00:02:06,479 --> 00:02:09,640
+problem because the package you wanted
+
+53
+00:02:07,719 --> 00:02:12,760
+to come to Pittsburgh goes to Tokyo
+
+54
+00:02:09,640 --> 00:02:13,680
+instead and uh you might not want that
+
+55
+00:02:12,760 --> 00:02:16,080
+to
+
+56
+00:02:13,680 --> 00:02:18,000
+happen you might also have it say
+
+57
+00:02:16,080 --> 00:02:20,400
+bleeping send this package to Pittsburgh
+
+58
+00:02:18,000 --> 00:02:22,200
+instead of pleas um and that would be a
+
+59
+00:02:20,400 --> 00:02:24,200
+problem in a customer service system
+
+60
+00:02:22,200 --> 00:02:28,400
+right because your customer would uh
+
+61
+00:02:24,200 --> 00:02:28,400
+leave and never come back
+
+62
+00:02:28,840 --> 00:02:32,040
+so
+
+63
+00:02:30,360 --> 00:02:33,720
+determiner like this is not going to
+
+64
+00:02:32,040 --> 00:02:35,640
+cause a huge issue U messing up other
+
+65
+00:02:33,720 --> 00:02:37,519
+things is going to cause a larger
+
+66
+00:02:35,640 --> 00:02:39,519
+issue but from the point of view of
+
+67
+00:02:37,519 --> 00:02:42,680
+Maximum likelihood all of these are just
+
+68
+00:02:39,519 --> 00:02:44,560
+tokens and messing up one token is the
+
+69
+00:02:42,680 --> 00:02:47,519
+same as messing up another token so
+
+70
+00:02:44,560 --> 00:02:50,040
+that's uh you know an
+
+71
+00:02:47,519 --> 00:02:52,080
+issue another problem is that the gold
+
+72
+00:02:50,040 --> 00:02:54,640
+standard and maximum likelihood
+
+73
+00:02:52,080 --> 00:02:57,480
+estimation can be bad it can be like not
+
+74
+00:02:54,640 --> 00:02:59,239
+what you want and uh corpa are full of
+
+75
+00:02:57,480 --> 00:03:02,400
+outputs that we wouldn't want a language
+
+76
+00:02:59,239 --> 00:03:05,400
+model producing so for example uh toxic
+
+77
+00:03:02,400 --> 00:03:07,799
+comments on Reddit uh
+
+78
+00:03:05,400 --> 00:03:09,959
+disinformation um another thing that a
+
+79
+00:03:07,799 --> 00:03:13,000
+lot of people don't think about uh quite
+
+80
+00:03:09,959 --> 00:03:15,640
+as much is a lot of the data online is
+
+81
+00:03:13,000 --> 00:03:17,680
+uh from is automatically generated
+
+82
+00:03:15,640 --> 00:03:19,720
+nowadays for example from machine
+
+83
+00:03:17,680 --> 00:03:24,080
+translation a lot of the translations
+
+84
+00:03:19,720 --> 00:03:25,720
+online are from uh 2016 Google translate
+
+85
+00:03:24,080 --> 00:03:27,560
+uh when Google translate was a lot less
+
+86
+00:03:25,720 --> 00:03:29,120
+good than it is now and so you have like
+
+87
+00:03:27,560 --> 00:03:31,760
+poor quality translations that were
+
+88
+00:03:29,120 --> 00:03:31,760
+automatically
+
+89
+00:03:33,040 --> 00:03:37,959
+a final problem is uh something that's
+
+90
+00:03:35,280 --> 00:03:40,360
+called exposure bias and exposure bias
+
+91
+00:03:37,959 --> 00:03:44,000
+basically what it means is mle training
+
+92
+00:03:40,360 --> 00:03:46,000
+doesn't consider um the necessarity the
+
+93
+00:03:44,000 --> 00:03:48,599
+necessity for generation and it relies
+
+94
+00:03:46,000 --> 00:03:51,360
+on gold standard context so if we go
+
+95
+00:03:48,599 --> 00:03:54,159
+back to the mle equation when we're
+
+96
+00:03:51,360 --> 00:03:57,200
+calculating mle this y less than T is
+
+97
+00:03:54,159 --> 00:03:59,200
+always correct it's always a good output
+
+98
+00:03:57,200 --> 00:04:01,439
+and so what the model does is it learns
+
+99
+00:03:59,200 --> 00:04:04,280
+to over rely on good
+
+100
+00:04:01,439 --> 00:04:06,079
+outputs and one example of a problem
+
+101
+00:04:04,280 --> 00:04:08,360
+that this causes is models tend to
+
+102
+00:04:06,079 --> 00:04:10,560
+repeat themselves over and over again
+
+103
+00:04:08,360 --> 00:04:12,319
+for example um when you use some
+
+104
+00:04:10,560 --> 00:04:15,079
+generation algorithms and the reason why
+
+105
+00:04:12,319 --> 00:04:18,519
+this happens is because in a gold
+
+106
+00:04:15,079 --> 00:04:22,079
+standard output if a word has appeared
+
+107
+00:04:18,519 --> 00:04:25,840
+previously that word is more likely to
+
+108
+00:04:22,079 --> 00:04:28,560
+happen next so like if you say um like I
+
+109
+00:04:25,840 --> 00:04:29,759
+am going um I am going to Pittsburgh
+
+110
+00:04:28,560 --> 00:04:31,880
+you're much more likely to say
+
+111
+00:04:29,759 --> 00:04:33,000
+Pittsburgh again in the future because
+
+112
+00:04:31,880 --> 00:04:35,720
+you're talking about Pittsburgh
+
+113
+00:04:33,000 --> 00:04:37,400
+topically as coherent so what you get is
+
+114
+00:04:35,720 --> 00:04:38,639
+you get mle trained models saying I'm
+
+115
+00:04:37,400 --> 00:04:40,160
+going to Pittsburgh I am going to
+
+116
+00:04:38,639 --> 00:04:41,680
+Pittsburgh I am going to Pittsburgh I
+
+117
+00:04:40,160 --> 00:04:45,280
+going to Pittsburgh you've probably seen
+
+118
+00:04:41,680 --> 00:04:47,320
+this before uh at some point and so um
+
+119
+00:04:45,280 --> 00:04:49,320
+exposure bias is basically that the
+
+120
+00:04:47,320 --> 00:04:51,039
+model has never been exposed to mistakes
+
+121
+00:04:49,320 --> 00:04:55,240
+in the past and so it can't deal with
+
+122
+00:04:51,039 --> 00:04:56,840
+them so what this does is um if you have
+
+123
+00:04:55,240 --> 00:04:58,560
+an alternative training algorithm you
+
+124
+00:04:56,840 --> 00:05:02,120
+can fix this by generating a whole bunch
+
+125
+00:04:58,560 --> 00:05:04,880
+of outputs uh down like scoring some of
+
+126
+00:05:02,120 --> 00:05:06,880
+them poorly and penalizing the model for
+
+127
+00:05:04,880 --> 00:05:09,960
+uh generating po outputs and so that can
+
+128
+00:05:06,880 --> 00:05:09,960
+fix these problems as
+
+129
+00:05:10,800 --> 00:05:18,440
+well uh any questions about this all
+
+130
+00:05:15,199 --> 00:05:20,800
+good Okay cool so now I'd like to get
+
+131
+00:05:18,440 --> 00:05:23,919
+into how we measure how good an output
+
+132
+00:05:20,800 --> 00:05:26,360
+is and there's different ways of doing
+
+133
+00:05:23,919 --> 00:05:30,319
+this um the first one is objective
+
+134
+00:05:26,360 --> 00:05:32,680
+assessment so for some uh tasks or for
+
+135
+00:05:30,319 --> 00:05:35,400
+many tasks there's kind of objectively a
+
+136
+00:05:32,680 --> 00:05:37,280
+correct answer there's also human
+
+137
+00:05:35,400 --> 00:05:40,360
+subjective annotations so you can ask
+
+138
+00:05:37,280 --> 00:05:42,919
+humans to do annotation for you there's
+
+139
+00:05:40,360 --> 00:05:45,400
+machine prediction of human
+
+140
+00:05:42,919 --> 00:05:48,319
+preferences and there's also use in
+
+141
+00:05:45,400 --> 00:05:50,840
+another system in a downstream
+
+142
+00:05:48,319 --> 00:05:52,960
+task so the way objective assessment
+
+143
+00:05:50,840 --> 00:05:54,919
+works is you have an annotated correct
+
+144
+00:05:52,960 --> 00:05:57,080
+answer in match against this so like if
+
+145
+00:05:54,919 --> 00:06:00,600
+you're solving math problems uh
+
+146
+00:05:57,080 --> 00:06:02,560
+answering objective questions and and
+
+147
+00:06:00,600 --> 00:06:04,280
+you know you can pick any arbitrary
+
+148
+00:06:02,560 --> 00:06:06,840
+example you can pick your classification
+
+149
+00:06:04,280 --> 00:06:09,800
+example from uh like your text
+
+150
+00:06:06,840 --> 00:06:11,880
+classification tasks an even clearer
+
+151
+00:06:09,800 --> 00:06:13,880
+example is if you have math problems
+
+152
+00:06:11,880 --> 00:06:15,639
+there's kind of objectively one answer
+
+153
+00:06:13,880 --> 00:06:18,080
+to any math problem and there's no other
+
+154
+00:06:15,639 --> 00:06:19,680
+answer that could be correct so this
+
+155
+00:06:18,080 --> 00:06:21,160
+makes your life easy if you're handling
+
+156
+00:06:19,680 --> 00:06:22,560
+this type of problem but of course
+
+157
+00:06:21,160 --> 00:06:24,120
+there's many other types of problems we
+
+158
+00:06:22,560 --> 00:06:26,039
+want to handle that don't have objective
+
+159
+00:06:24,120 --> 00:06:29,039
+answers like
+
+160
+00:06:26,039 --> 00:06:31,440
+this so let's say we're handling a gener
+
+161
+00:06:29,039 --> 00:06:34,680
+a generation task where we don't have an
+
+162
+00:06:31,440 --> 00:06:36,360
+objective answer um in this Cas kind of
+
+163
+00:06:34,680 --> 00:06:39,440
+one of our gold standards is human
+
+164
+00:06:36,360 --> 00:06:42,360
+evaluation so we might have a source
+
+165
+00:06:39,440 --> 00:06:44,919
+input like a prompt or an input text for
+
+166
+00:06:42,360 --> 00:06:47,240
+machine translation we have one or
+
+167
+00:06:44,919 --> 00:06:49,960
+several hypotheses and we ask a human
+
+168
+00:06:47,240 --> 00:06:53,280
+annotator to basically give uh a score
+
+169
+00:06:49,960 --> 00:06:55,759
+for them or do some sort of other
+
+170
+00:06:53,280 --> 00:06:59,759
+annotation and the different varieties
+
+171
+00:06:55,759 --> 00:07:03,080
+of annotation that we can give are um
+
+172
+00:06:59,759 --> 00:07:04,599
+something called direct assessment so uh
+
+173
+00:07:03,080 --> 00:07:06,599
+direct assessment is a term that comes
+
+174
+00:07:04,599 --> 00:07:09,280
+from machine translation uh so you might
+
+175
+00:07:06,599 --> 00:07:11,039
+not see it used uh lots of other places
+
+176
+00:07:09,280 --> 00:07:13,120
+but it's basically just give a score
+
+177
+00:07:11,039 --> 00:07:15,759
+directly to how good the output is so
+
+178
+00:07:13,120 --> 00:07:17,199
+you can say like if you say please send
+
+179
+00:07:15,759 --> 00:07:18,960
+this translation is please send this
+
+180
+00:07:17,199 --> 00:07:21,759
+package to Tokyo we give it a score of
+
+181
+00:07:18,960 --> 00:07:24,360
+two out of 10 or something like
+
+182
+00:07:21,759 --> 00:07:28,000
+this
+
+183
+00:07:24,360 --> 00:07:30,840
+so the the question here is like what
+
+184
+00:07:28,000 --> 00:07:32,400
+does like let's say I gave a score of
+
+185
+00:07:30,840 --> 00:07:34,520
+two out of 10 for please send this
+
+186
+00:07:32,400 --> 00:07:37,680
+package to Tokyo what score should I
+
+187
+00:07:34,520 --> 00:07:40,240
+give for please send a package to Tokyo
+
+188
+00:07:37,680 --> 00:07:42,360
+anyone have any ideas the the correct
+
+189
+00:07:40,240 --> 00:07:46,520
+answer is please send this package to
+
+190
+00:07:42,360 --> 00:07:48,000
+take out of eight out of 10 yeah but you
+
+191
+00:07:46,520 --> 00:07:50,440
+might disagree on that right it's kind
+
+192
+00:07:48,000 --> 00:07:52,159
+of like subjective um one of the
+
+193
+00:07:50,440 --> 00:07:54,039
+difficulties of direct assessment is
+
+194
+00:07:52,159 --> 00:07:55,520
+giving a number like this is pretty
+
+195
+00:07:54,039 --> 00:07:57,800
+difficult if you don't have a very clear
+
+196
+00:07:55,520 --> 00:07:59,720
+rubric and very skilled annotators and
+
+197
+00:07:57,800 --> 00:08:02,879
+it's hard to get consistency between
+
+198
+00:07:59,720 --> 00:08:04,400
+people when you do this so the advantage
+
+199
+00:08:02,879 --> 00:08:05,599
+is it kind of gives you an idea of how
+
+200
+00:08:04,400 --> 00:08:07,520
+good things are overall but the
+
+201
+00:08:05,599 --> 00:08:09,280
+disadvantage is it's more difficult to
+
+202
+00:08:07,520 --> 00:08:11,319
+annotate and get
+
+203
+00:08:09,280 --> 00:08:13,159
+consistency um another thing that I
+
+204
+00:08:11,319 --> 00:08:15,319
+should point out is often scores are
+
+205
+00:08:13,159 --> 00:08:18,680
+assigned separately based on desirable
+
+206
+00:08:15,319 --> 00:08:20,960
+traits so um we don't necessarily just
+
+207
+00:08:18,680 --> 00:08:23,479
+say how good is it we say how fluent is
+
+208
+00:08:20,960 --> 00:08:26,120
+it like is it fluent uh
+
+209
+00:08:23,479 --> 00:08:28,159
+English in Translation there's a concept
+
+210
+00:08:26,120 --> 00:08:30,720
+called adequacy which is how well does
+
+211
+00:08:28,159 --> 00:08:34,599
+the output reflect the input
+
+212
+00:08:30,720 --> 00:08:36,519
+semantics um and if you're assessing
+
+213
+00:08:34,599 --> 00:08:38,440
+translation systems actually it's common
+
+214
+00:08:36,519 --> 00:08:40,519
+to assess fluency without even looking
+
+215
+00:08:38,440 --> 00:08:43,200
+at the input because then you can just
+
+216
+00:08:40,519 --> 00:08:44,880
+say how fluent is it but for adequacy
+
+217
+00:08:43,200 --> 00:08:46,320
+you definitely need to understand the
+
+218
+00:08:44,880 --> 00:08:49,600
+input so you need to be a bilingual
+
+219
+00:08:46,320 --> 00:08:54,680
+speaker to be able to assess
+
+220
+00:08:49,600 --> 00:08:57,560
+that um factuality um and so factuality
+
+221
+00:08:54,680 --> 00:09:00,160
+is tricky um it can either be factuality
+
+222
+00:08:57,560 --> 00:09:03,880
+grounded in a particular input text in
+
+223
+00:09:00,160 --> 00:09:05,600
+which case um the facts would have to be
+
+224
+00:09:03,880 --> 00:09:07,680
+you know things that were said in the
+
+225
+00:09:05,600 --> 00:09:09,399
+input or it can be just kind of is the
+
+226
+00:09:07,680 --> 00:09:11,120
+statement factual in general in which
+
+227
+00:09:09,399 --> 00:09:13,720
+case you need to go online you need to
+
+228
+00:09:11,120 --> 00:09:16,480
+search for things and like uh check
+
+229
+00:09:13,720 --> 00:09:18,480
+whether the statement is factual or not
+
+230
+00:09:16,480 --> 00:09:20,480
+um other things are like coherence does
+
+231
+00:09:18,480 --> 00:09:21,480
+the output fit coherently within the
+
+232
+00:09:20,480 --> 00:09:23,680
+larger
+
+233
+00:09:21,480 --> 00:09:25,680
+discs um and there's many many other
+
+234
+00:09:23,680 --> 00:09:28,120
+ones of these this is also task
+
+235
+00:09:25,680 --> 00:09:29,760
+dependent so like the things you will
+
+236
+00:09:28,120 --> 00:09:31,000
+evaluate for machine transl are
+
+237
+00:09:29,760 --> 00:09:32,880
+different than the ones you would do for
+
+238
+00:09:31,000 --> 00:09:35,760
+dialog which are different than the ones
+
+239
+00:09:32,880 --> 00:09:38,200
+you would do for a general purpose
+
+240
+00:09:35,760 --> 00:09:41,279
+chatot uh which is different kind things
+
+241
+00:09:38,200 --> 00:09:44,120
+you would do for um summarization for
+
+242
+00:09:41,279 --> 00:09:46,320
+example so if you're interested in doing
+
+243
+00:09:44,120 --> 00:09:47,519
+something like this uh then I definitely
+
+244
+00:09:46,320 --> 00:09:48,800
+encourage you to look at what other
+
+245
+00:09:47,519 --> 00:09:51,399
+people have done for the tasks you're
+
+246
+00:09:48,800 --> 00:09:53,079
+interested in uh previously and uh find
+
+247
+00:09:51,399 --> 00:09:54,880
+out the different types of traits that
+
+248
+00:09:53,079 --> 00:09:58,320
+did
+
+249
+00:09:54,880 --> 00:10:00,760
+last uh any any questions about this
+
+250
+00:09:58,320 --> 00:10:03,079
+also
+
+251
+00:10:00,760 --> 00:10:06,920
+okay the next type of feedback is
+
+252
+00:10:03,079 --> 00:10:09,839
+preference ratings um and so this is uh
+
+253
+00:10:06,920 --> 00:10:12,600
+basically what you do is you have two or
+
+254
+00:10:09,839 --> 00:10:14,240
+more outputs from different models or
+
+255
+00:10:12,600 --> 00:10:16,440
+different Generations from an individual
+
+256
+00:10:14,240 --> 00:10:18,839
+model and you ask a human which one is
+
+257
+00:10:16,440 --> 00:10:22,320
+better like is one better than the other
+
+258
+00:10:18,839 --> 00:10:23,839
+or are they tied and so in this case um
+
+259
+00:10:22,320 --> 00:10:26,320
+you might have please send this package
+
+260
+00:10:23,839 --> 00:10:28,880
+to Tokyo please send a package to
+
+261
+00:10:26,320 --> 00:10:31,040
+Tokyo we might disagree on how like good
+
+262
+00:10:28,880 --> 00:10:33,959
+or bad each of them are but I think most
+
+263
+00:10:31,040 --> 00:10:35,959
+people would agree that this one is like
+
+264
+00:10:33,959 --> 00:10:37,480
+despite the fact that it got this wrong
+
+265
+00:10:35,959 --> 00:10:40,160
+the second one is better than the first
+
+266
+00:10:37,480 --> 00:10:42,240
+one so this is a little bit of an easier
+
+267
+00:10:40,160 --> 00:10:45,040
+task it's easier to uh get people to
+
+268
+00:10:42,240 --> 00:10:46,839
+annotate these things
+
+269
+00:10:45,040 --> 00:10:50,519
+consistently however it has the
+
+270
+00:10:46,839 --> 00:10:52,839
+disadvantage that you can't really tell
+
+271
+00:10:50,519 --> 00:10:55,360
+uh whether systems are really good or
+
+272
+00:10:52,839 --> 00:10:57,200
+really bad so let's say you have a bunch
+
+273
+00:10:55,360 --> 00:11:00,279
+of really bad systems that you're
+
+274
+00:10:57,200 --> 00:11:01,839
+comparing with each other um you might
+
+275
+00:11:00,279 --> 00:11:03,680
+find that one is better than the other
+
+276
+00:11:01,839 --> 00:11:06,000
+but that still doesn't mean it's ready
+
+277
+00:11:03,680 --> 00:11:07,399
+to be deployed or if you have a bunch of
+
+278
+00:11:06,000 --> 00:11:11,040
+really good systems they're all
+
+279
+00:11:07,399 --> 00:11:13,000
+basically you know very very similar to
+
+280
+00:11:11,040 --> 00:11:14,399
+another but one is like slightly more
+
+281
+00:11:13,000 --> 00:11:18,639
+fluent than the other you might still
+
+282
+00:11:14,399 --> 00:11:20,680
+get a similar result um and so that also
+
+283
+00:11:18,639 --> 00:11:22,760
+makes it uh you know a little bit
+
+284
+00:11:20,680 --> 00:11:24,880
+difficult to use practically in some
+
+285
+00:11:22,760 --> 00:11:27,040
+ways I didn't put it on the slide but
+
+286
+00:11:24,880 --> 00:11:30,680
+there's another way you can kind of get
+
+287
+00:11:27,040 --> 00:11:33,920
+the best of both worlds um which is a
+
+288
+00:11:30,680 --> 00:11:35,560
+side by side assessment and side by-side
+
+289
+00:11:33,920 --> 00:11:38,440
+assessment basically what you would do
+
+290
+00:11:35,560 --> 00:11:40,560
+is you would say um please send this
+
+291
+00:11:38,440 --> 00:11:43,399
+package to Tokyo please send a package
+
+292
+00:11:40,560 --> 00:11:47,279
+to Pittsburgh give each of them a direct
+
+293
+00:11:43,399 --> 00:11:48,839
+score um but you can use decimal places
+
+294
+00:11:47,279 --> 00:11:51,120
+and you can't use the same score for all
+
+295
+00:11:48,839 --> 00:11:55,920
+of them and so it's
+
+296
+00:11:51,120 --> 00:11:57,480
+like five 500 and 4.99 out of five or
+
+297
+00:11:55,920 --> 00:11:59,519
+something like that like you like one
+
+298
+00:11:57,480 --> 00:12:02,639
+slightly better than the other or or
+
+299
+00:11:59,519 --> 00:12:04,480
+something like that um so there are ways
+
+300
+00:12:02,639 --> 00:12:07,240
+to kind of get Best of Both Worlds if
+
+301
+00:12:04,480 --> 00:12:11,720
+you're interested in doing
+
+302
+00:12:07,240 --> 00:12:11,720
+that um
+
+303
+00:12:14,920 --> 00:12:20,519
+so one problem one other problem with
+
+304
+00:12:18,279 --> 00:12:22,519
+preference rankings is that there's a
+
+305
+00:12:20,519 --> 00:12:24,440
+limited number of things that humans can
+
+306
+00:12:22,519 --> 00:12:28,160
+compare before they get really
+
+307
+00:12:24,440 --> 00:12:32,360
+overwhelmed so if you say I
+
+308
+00:12:28,160 --> 00:12:35,560
+want like I want to
+
+309
+00:12:32,360 --> 00:12:36,920
+rate 15 systems or 20 systems with
+
+310
+00:12:35,560 --> 00:12:39,120
+respect to how good they are with
+
+311
+00:12:36,920 --> 00:12:40,639
+respect to each other it's going to be
+
+312
+00:12:39,120 --> 00:12:43,680
+impossible for humans to come up with a
+
+313
+00:12:40,639 --> 00:12:46,959
+good preference ranking between them and
+
+314
+00:12:43,680 --> 00:12:49,480
+so the typical way around this um which
+
+315
+00:12:46,959 --> 00:12:52,360
+is also used in uh things like the
+
+316
+00:12:49,480 --> 00:12:55,440
+chatbot Arena by lmis and other things
+
+317
+00:12:52,360 --> 00:12:58,720
+like this is to use uh something like an
+
+318
+00:12:55,440 --> 00:13:00,959
+ELO or true skill ranking and what these
+
+319
+00:12:58,720 --> 00:13:03,079
+are is these are things that were
+
+320
+00:13:00,959 --> 00:13:05,760
+created for the ranking of like chess
+
+321
+00:13:03,079 --> 00:13:09,160
+players or video game players or other
+
+322
+00:13:05,760 --> 00:13:11,720
+things where they like b battle against
+
+323
+00:13:09,160 --> 00:13:13,920
+each other in multiple matches uh
+
+324
+00:13:11,720 --> 00:13:16,440
+pair-wise and then you put all of the
+
+325
+00:13:13,920 --> 00:13:18,399
+wins and losses into these ranking
+
+326
+00:13:16,440 --> 00:13:20,600
+algorithms and they give you a score
+
+327
+00:13:18,399 --> 00:13:22,920
+about how good like each of the each of
+
+328
+00:13:20,600 --> 00:13:27,079
+the players are so if you do something
+
+329
+00:13:22,920 --> 00:13:29,480
+like this you can um get basically a
+
+330
+00:13:27,079 --> 00:13:32,120
+ranking of systems despite the that you
+
+331
+00:13:29,480 --> 00:13:35,240
+only did pairwise assessments so these
+
+332
+00:13:32,120 --> 00:13:35,240
+are also a good thing to know
+
+333
+00:13:37,399 --> 00:13:43,839
+about a final variety of human feedback
+
+334
+00:13:40,600 --> 00:13:45,320
+uh that we create is uh air annotation
+
+335
+00:13:43,839 --> 00:13:47,519
+and this can be useful for a number of
+
+336
+00:13:45,320 --> 00:13:49,839
+reasons um but basically the way it
+
+337
+00:13:47,519 --> 00:13:53,839
+works is you annotate individual errors
+
+338
+00:13:49,839 --> 00:13:55,639
+within the outputs and um oh one thing I
+
+339
+00:13:53,839 --> 00:13:58,120
+should mention is that um I'm giving a
+
+340
+00:13:55,639 --> 00:14:00,880
+lot of examples from machine translation
+
+341
+00:13:58,120 --> 00:14:02,800
+um I feel like machine translation has
+
+342
+00:14:00,880 --> 00:14:04,519
+been doing evaluation of generated
+
+343
+00:14:02,800 --> 00:14:07,600
+outputs for a lot longer than a lot of
+
+344
+00:14:04,519 --> 00:14:09,000
+other uh fields of NLP have and
+
+345
+00:14:07,600 --> 00:14:11,800
+therefore their methodology is more
+
+346
+00:14:09,000 --> 00:14:13,480
+developed than a lot of other fields um
+
+347
+00:14:11,800 --> 00:14:16,199
+but a lot of these things can also be
+
+348
+00:14:13,480 --> 00:14:18,079
+applied to uh other uh other tasks as
+
+349
+00:14:16,199 --> 00:14:19,079
+well but anyway getting back to this
+
+350
+00:14:18,079 --> 00:14:20,680
+there's something for machine
+
+351
+00:14:19,079 --> 00:14:23,639
+translation called multi-dimensional
+
+352
+00:14:20,680 --> 00:14:26,240
+quality metrics and the multidimensional
+
+353
+00:14:23,639 --> 00:14:29,160
+quality metrics basically what they do
+
+354
+00:14:26,240 --> 00:14:32,199
+is they annotate spans in the output
+
+355
+00:14:29,160 --> 00:14:34,800
+where each Span in the output is given a
+
+356
+00:14:32,199 --> 00:14:38,079
+severity ranking of the error and it's
+
+357
+00:14:34,800 --> 00:14:40,199
+given a type of the error and there's
+
+358
+00:14:38,079 --> 00:14:42,600
+about eight different types of Errors
+
+359
+00:14:40,199 --> 00:14:44,839
+like this doesn't violate or this
+
+360
+00:14:42,600 --> 00:14:47,399
+violates linguistic conventions of using
+
+361
+00:14:44,839 --> 00:14:49,880
+the word this instead of uh here by
+
+362
+00:14:47,399 --> 00:14:51,639
+using the word uh instead of this here
+
+363
+00:14:49,880 --> 00:14:55,079
+and then this is an accuracy error
+
+364
+00:14:51,639 --> 00:14:57,839
+because it's not accurately con uh uh
+
+365
+00:14:55,079 --> 00:15:01,720
+conveying the output and then this error
+
+366
+00:14:57,839 --> 00:15:04,600
+is minor uh this error is Major um and
+
+367
+00:15:01,720 --> 00:15:06,399
+then there's also like severe severe
+
+368
+00:15:04,600 --> 00:15:07,440
+versus major but minor and major is a
+
+369
+00:15:06,399 --> 00:15:09,680
+more important
+
+370
+00:15:07,440 --> 00:15:11,839
+distinction um so the advantage of this
+
+371
+00:15:09,680 --> 00:15:14,279
+is a couple fold number one it gives you
+
+372
+00:15:11,839 --> 00:15:16,440
+more fine grained feedback uh in that
+
+373
+00:15:14,279 --> 00:15:19,199
+you can say okay this system has a lot
+
+374
+00:15:16,440 --> 00:15:22,199
+of uh accuracy errors this system has a
+
+375
+00:15:19,199 --> 00:15:24,880
+lot of linguistic conventions errors um
+
+376
+00:15:22,199 --> 00:15:28,600
+it also can be more consistent because
+
+377
+00:15:24,880 --> 00:15:29,839
+if you just say to people which output
+
+378
+00:15:28,600 --> 00:15:31,800
+is better
+
+379
+00:15:29,839 --> 00:15:34,560
+or what is the score of this output
+
+380
+00:15:31,800 --> 00:15:36,360
+people have trouble deciding about that
+
+381
+00:15:34,560 --> 00:15:39,560
+because it's a more subjective
+
+382
+00:15:36,360 --> 00:15:41,680
+evaluation but if I say is this word
+
+383
+00:15:39,560 --> 00:15:43,000
+correct it's a little bit easier for
+
+384
+00:15:41,680 --> 00:15:44,759
+people to do so you can get more
+
+385
+00:15:43,000 --> 00:15:46,920
+consistent annotations
+
+386
+00:15:44,759 --> 00:15:49,720
+here the problem with this is this can
+
+387
+00:15:46,920 --> 00:15:50,839
+be very time consuming so um you know
+
+388
+00:15:49,720 --> 00:15:52,480
+obviously you need to go through and
+
+389
+00:15:50,839 --> 00:15:56,440
+annotate every single error if it's for
+
+390
+00:15:52,480 --> 00:15:56,440
+a long outputs or something your
+
+391
+00:15:56,959 --> 00:16:03,519
+problem so anyway these are just three
+
+392
+00:15:59,800 --> 00:16:05,680
+uh ways of collecting human feedback um
+
+393
+00:16:03,519 --> 00:16:08,639
+and then there's an alternative which is
+
+394
+00:16:05,680 --> 00:16:10,079
+automatic evaluation of outputs and um
+
+395
+00:16:08,639 --> 00:16:14,399
+there's a bunch of different ways we can
+
+396
+00:16:10,079 --> 00:16:16,800
+do this the basic idea here is we have a
+
+397
+00:16:14,399 --> 00:16:20,199
+source um we have a couple
+
+398
+00:16:16,800 --> 00:16:22,800
+hypotheses and uh we have an automatic
+
+399
+00:16:20,199 --> 00:16:26,000
+system that generates outputs uh like
+
+400
+00:16:22,800 --> 00:16:28,279
+scores and we optionally have a
+
+401
+00:16:26,000 --> 00:16:30,839
+reference output so the reference output
+
+402
+00:16:28,279 --> 00:16:33,519
+is a human created gold standard output
+
+403
+00:16:30,839 --> 00:16:35,120
+with respect to how good that um uh with
+
+404
+00:16:33,519 --> 00:16:38,240
+respect to like what the output should
+
+405
+00:16:35,120 --> 00:16:38,240
+be in an ideal
+
+406
+00:16:38,279 --> 00:16:47,079
+case and basically the goal of automatic
+
+407
+00:16:43,199 --> 00:16:50,199
+evaluation is to
+
+408
+00:16:47,079 --> 00:16:52,839
+predict human preferences or to predict
+
+409
+00:16:50,199 --> 00:16:56,240
+what the human scores would be um
+
+410
+00:16:52,839 --> 00:16:58,600
+because still at this point um we mostly
+
+411
+00:16:56,240 --> 00:16:59,480
+view what humans think of the output to
+
+412
+00:16:58,600 --> 00:17:01,680
+be
+
+413
+00:16:59,480 --> 00:17:03,280
+uh kind of the
+
+414
+00:17:01,680 --> 00:17:06,199
+standard
+
+415
+00:17:03,280 --> 00:17:08,439
+and this is called a variety of things
+
+416
+00:17:06,199 --> 00:17:10,600
+depending on what field you're in um in
+
+417
+00:17:08,439 --> 00:17:12,559
+machine translation and summarization
+
+418
+00:17:10,600 --> 00:17:13,520
+it's called automatic evaluation also a
+
+419
+00:17:12,559 --> 00:17:16,520
+lot in
+
+420
+00:17:13,520 --> 00:17:18,400
+dialogue um if you're talking about
+
+421
+00:17:16,520 --> 00:17:21,000
+people from reinforcement learning or
+
+422
+00:17:18,400 --> 00:17:24,600
+other things um or chat Bots or things
+
+423
+00:17:21,000 --> 00:17:28,240
+like that uh a lot of people or uh like
+
+424
+00:17:24,600 --> 00:17:31,280
+AGI or whatever um a lot of people call
+
+425
+00:17:28,240 --> 00:17:32,520
+it uh word model um because that
+
+426
+00:17:31,280 --> 00:17:34,480
+specifically comes from the point of
+
+427
+00:17:32,520 --> 00:17:36,440
+view of like learning from this feedback
+
+428
+00:17:34,480 --> 00:17:37,960
+but essentially they're the same thing
+
+429
+00:17:36,440 --> 00:17:41,080
+uh from my point of view they're trying
+
+430
+00:17:37,960 --> 00:17:42,520
+to predict how good an output is and how
+
+431
+00:17:41,080 --> 00:17:44,240
+much you should reward the model for
+
+432
+00:17:42,520 --> 00:17:46,559
+producing that
+
+433
+00:17:44,240 --> 00:17:48,679
+output
+
+434
+00:17:46,559 --> 00:17:50,520
+um so there's a bunch of different
+
+435
+00:17:48,679 --> 00:17:51,720
+methods to do this I'm not going to
+
+436
+00:17:50,520 --> 00:17:53,799
+cover all of them I'm just going to
+
+437
+00:17:51,720 --> 00:17:55,240
+cover three paradigms for doing this so
+
+438
+00:17:53,799 --> 00:17:57,880
+you know where to look further if you're
+
+439
+00:17:55,240 --> 00:18:00,039
+interested in doing these things um the
+
+440
+00:17:57,880 --> 00:18:02,400
+first one is embedding based
+
+441
+00:18:00,039 --> 00:18:04,679
+evaluation and the way embedding based
+
+442
+00:18:02,400 --> 00:18:06,600
+evaluation works is usually it's
+
+443
+00:18:04,679 --> 00:18:11,400
+unsupervised calculation based on
+
+444
+00:18:06,600 --> 00:18:14,880
+embeding similarity between um
+
+445
+00:18:11,400 --> 00:18:18,080
+the output that the model generated and
+
+446
+00:18:14,880 --> 00:18:20,840
+a reference output that uh you have
+
+447
+00:18:18,080 --> 00:18:23,400
+created so sorry this is very small but
+
+448
+00:18:20,840 --> 00:18:25,559
+we have a reference here that says the
+
+449
+00:18:23,400 --> 00:18:27,640
+weather is cold today and we have a
+
+450
+00:18:25,559 --> 00:18:30,240
+candidate that says it is freezing today
+
+451
+00:18:27,640 --> 00:18:33,000
+so this is probably you know like a good
+
+452
+00:18:30,240 --> 00:18:35,480
+um a reasonably good
+
+453
+00:18:33,000 --> 00:18:37,640
+output and we run this through some
+
+454
+00:18:35,480 --> 00:18:39,120
+embedding model uh it was called Bert
+
+455
+00:18:37,640 --> 00:18:40,679
+score and so of course you can run it
+
+456
+00:18:39,120 --> 00:18:42,240
+through Bert but basically it can be any
+
+457
+00:18:40,679 --> 00:18:43,799
+embedding model that gives you embedding
+
+458
+00:18:42,240 --> 00:18:46,200
+for each token in the
+
+459
+00:18:43,799 --> 00:18:47,640
+sequence and so there are five tokens in
+
+460
+00:18:46,200 --> 00:18:49,720
+this sequence four tokens in this
+
+461
+00:18:47,640 --> 00:18:51,960
+sequence you get five tokens and then
+
+462
+00:18:49,720 --> 00:18:54,799
+four sorry five embeddings and then four
+
+463
+00:18:51,960 --> 00:18:57,400
+embeddings you calculate carewise cosine
+
+464
+00:18:54,799 --> 00:18:59,880
+similarity between all of them and this
+
+465
+00:18:57,400 --> 00:19:03,480
+gives you cosine
+
+466
+00:18:59,880 --> 00:19:06,480
+similarity Matrix and then you take the
+
+467
+00:19:03,480 --> 00:19:09,120
+ARG Max or you take the maximum
+
+468
+00:19:06,480 --> 00:19:11,280
+similarity along either the
+
+469
+00:19:09,120 --> 00:19:15,799
+rows or the
+
+470
+00:19:11,280 --> 00:19:19,559
+columns and here the rows correspond
+
+471
+00:19:15,799 --> 00:19:22,400
+to tokens in the reference and because
+
+472
+00:19:19,559 --> 00:19:24,039
+the rows correspond to tokens in the
+
+473
+00:19:22,400 --> 00:19:26,960
+reference
+
+474
+00:19:24,039 --> 00:19:28,320
+the how well you find something that is
+
+475
+00:19:26,960 --> 00:19:31,679
+similar to each of the tokens in the
+
+476
+00:19:28,320 --> 00:19:34,000
+reference is like a recall based method
+
+477
+00:19:31,679 --> 00:19:35,919
+because it's saying how many tokens in
+
+478
+00:19:34,000 --> 00:19:39,520
+the reference have a good match in the
+
+479
+00:19:35,919 --> 00:19:41,120
+output and then if you look at the
+
+480
+00:19:39,520 --> 00:19:42,799
+columns if you look at the max and the
+
+481
+00:19:41,120 --> 00:19:44,960
+columns this is like a precision based
+
+482
+00:19:42,799 --> 00:19:47,000
+metric because it's saying how many of
+
+483
+00:19:44,960 --> 00:19:49,360
+the things in the output are similar
+
+484
+00:19:47,000 --> 00:19:51,240
+have a similar match in the reference so
+
+485
+00:19:49,360 --> 00:19:54,480
+basically you can calculate recall and
+
+486
+00:19:51,240 --> 00:19:56,200
+precision over all of the tokens and
+
+487
+00:19:54,480 --> 00:20:00,200
+then feed this into something that looks
+
+488
+00:19:56,200 --> 00:20:02,400
+like fmeasure and you can also use tfidf
+
+489
+00:20:00,200 --> 00:20:06,000
+waiting um like what I talked about in
+
+490
+00:20:02,400 --> 00:20:07,799
+the rag lecture uh to upweight low
+
+491
+00:20:06,000 --> 00:20:09,520
+frequency words because low frequency
+
+492
+00:20:07,799 --> 00:20:11,440
+words tend to be more content words and
+
+493
+00:20:09,520 --> 00:20:13,120
+going back to my example you know if you
+
+494
+00:20:11,440 --> 00:20:14,280
+make a mistake from Pittsburgh to Tokyo
+
+495
+00:20:13,120 --> 00:20:17,880
+that's going to be more painful than
+
+496
+00:20:14,280 --> 00:20:21,000
+making a mistake from this to um so
+
+497
+00:20:17,880 --> 00:20:22,520
+actually if you'll uh if you were paying
+
+498
+00:20:21,000 --> 00:20:25,480
+close attention to the rag lecture this
+
+499
+00:20:22,520 --> 00:20:27,360
+looks really similar to the co bear um
+
+500
+00:20:25,480 --> 00:20:29,559
+the co bear retrieval objective that I
+
+501
+00:20:27,360 --> 00:20:30,960
+talked about in the r lecture um I don't
+
+502
+00:20:29,559 --> 00:20:32,840
+think it's a coincidence they both came
+
+503
+00:20:30,960 --> 00:20:34,360
+out around the same time uh so people
+
+504
+00:20:32,840 --> 00:20:36,360
+were thinking about the same thing but
+
+505
+00:20:34,360 --> 00:20:37,600
+um this is one method that's pretty
+
+506
+00:20:36,360 --> 00:20:40,200
+widely
+
+507
+00:20:37,600 --> 00:20:43,480
+use the bird Square code base is also
+
+508
+00:20:40,200 --> 00:20:45,440
+really nice and easy to use so um if uh
+
+509
+00:20:43,480 --> 00:20:47,640
+you want to try it out feel free to take
+
+510
+00:20:45,440 --> 00:20:47,640
+a
+
+511
+00:20:48,159 --> 00:20:53,840
+look cool um the next one I'd like to
+
+512
+00:20:51,600 --> 00:20:56,080
+talk about is a regression based
+
+513
+00:20:53,840 --> 00:20:58,760
+evaluation and the way this works is
+
+514
+00:20:56,080 --> 00:21:02,600
+this is usually used in a supervised uh
+
+515
+00:20:58,760 --> 00:21:04,320
+setting so uh the way what you have to
+
+516
+00:21:02,600 --> 00:21:07,600
+do is you have to calculate a whole
+
+517
+00:21:04,320 --> 00:21:09,799
+bunch of like actual human
+
+518
+00:21:07,600 --> 00:21:12,440
+judgments and
+
+519
+00:21:09,799 --> 00:21:15,000
+usually these judgments can either be
+
+520
+00:21:12,440 --> 00:21:16,960
+direct assessment uh where you actually
+
+521
+00:21:15,000 --> 00:21:19,120
+have a score or they can be pairwise
+
+522
+00:21:16,960 --> 00:21:20,840
+judgments and then if you have direct
+
+523
+00:21:19,120 --> 00:21:23,640
+assessment you use a regression based
+
+524
+00:21:20,840 --> 00:21:26,039
+loss like uh minimum squared error if
+
+525
+00:21:23,640 --> 00:21:27,520
+you have pairwise uh you use a ranking
+
+526
+00:21:26,039 --> 00:21:29,039
+based loss that tries to upweight the
+
+527
+00:21:27,520 --> 00:21:31,360
+ones that are higher scoring downward
+
+528
+00:21:29,039 --> 00:21:33,200
+the ones that are lower scoring one
+
+529
+00:21:31,360 --> 00:21:35,720
+typical example of this is Comet which
+
+530
+00:21:33,200 --> 00:21:37,200
+is or has been at least for a very long
+
+531
+00:21:35,720 --> 00:21:39,880
+time the state-of-the art and machine
+
+532
+00:21:37,200 --> 00:21:41,279
+translation evaluation and the reason
+
+533
+00:21:39,880 --> 00:21:43,440
+why it works so well is because we have
+
+534
+00:21:41,279 --> 00:21:44,720
+a bunch of evaluations for machine
+
+535
+00:21:43,440 --> 00:21:46,080
+translation they've been doing
+
+536
+00:21:44,720 --> 00:21:47,600
+evaluation and machine translation
+
+537
+00:21:46,080 --> 00:21:50,480
+systems for years and you can use that
+
+538
+00:21:47,600 --> 00:21:52,720
+as lots of supervised training data so
+
+539
+00:21:50,480 --> 00:21:54,640
+basically you just take um these
+
+540
+00:21:52,720 --> 00:21:56,440
+evaluation data you have human
+
+541
+00:21:54,640 --> 00:21:59,080
+annotations you have the output
+
+542
+00:21:56,440 --> 00:22:00,320
+according to a model like comet um you
+
+543
+00:21:59,080 --> 00:22:02,679
+calculate the difference between them
+
+544
+00:22:00,320 --> 00:22:05,640
+and you update model
+
+545
+00:22:02,679 --> 00:22:07,080
+parameters um the problem this is great
+
+546
+00:22:05,640 --> 00:22:08,520
+if you have lots of training data the
+
+547
+00:22:07,080 --> 00:22:10,640
+problem with this is for a lot of tasks
+
+548
+00:22:08,520 --> 00:22:12,360
+we don't have lots of training data so
+
+549
+00:22:10,640 --> 00:22:14,720
+um you know training these is a little
+
+550
+00:22:12,360 --> 00:22:14,720
+bit less
+
+551
+00:22:15,400 --> 00:22:22,919
+feasible and now recently uh what we
+
+552
+00:22:19,600 --> 00:22:25,279
+have been moving into is is a QA based
+
+553
+00:22:22,919 --> 00:22:27,120
+evaluation which is basically where we
+
+554
+00:22:25,279 --> 00:22:30,760
+ask a language model how good the output
+
+555
+00:22:27,120 --> 00:22:32,279
+is and so uh gmba is an example one of
+
+556
+00:22:30,760 --> 00:22:34,559
+the early examples of this for machine
+
+557
+00:22:32,279 --> 00:22:37,320
+translation evaluation uh where they
+
+558
+00:22:34,559 --> 00:22:39,840
+basically just ask a g gp4 like score
+
+559
+00:22:37,320 --> 00:22:41,600
+the following translation from Source
+
+560
+00:22:39,840 --> 00:22:44,000
+language to target language with respect
+
+561
+00:22:41,600 --> 00:22:47,080
+to the human reference um on a
+
+562
+00:22:44,000 --> 00:22:49,200
+continuous scale from Z to 100 uh where
+
+563
+00:22:47,080 --> 00:22:51,320
+the score of zero means no meaning
+
+564
+00:22:49,200 --> 00:22:54,039
+preserved and the score of 100 means a
+
+565
+00:22:51,320 --> 00:22:56,880
+perfect meaning in grammar uh you feed
+
+566
+00:22:54,039 --> 00:22:58,760
+in the source um you feed in the T the
+
+567
+00:22:56,880 --> 00:23:01,000
+human reference optionally if you have a
+
+568
+00:22:58,760 --> 00:23:03,320
+human reference and then you feed in the
+
+569
+00:23:01,000 --> 00:23:06,760
+Target um and you get a
+
+570
+00:23:03,320 --> 00:23:09,919
+score and um so this this works pretty
+
+571
+00:23:06,760 --> 00:23:12,720
+well this can give you uh better results
+
+572
+00:23:09,919 --> 00:23:15,159
+um there's a especially if you have a
+
+573
+00:23:12,720 --> 00:23:16,960
+strong language model the problem is
+
+574
+00:23:15,159 --> 00:23:18,279
+it's very unpredictable whether this is
+
+575
+00:23:16,960 --> 00:23:20,120
+going to work well and it's very
+
+576
+00:23:18,279 --> 00:23:23,039
+dependent on the prompt that you're
+
+577
+00:23:20,120 --> 00:23:25,279
+using so um right now A lot of people
+
+578
+00:23:23,039 --> 00:23:27,279
+are using gp4 without actually
+
+579
+00:23:25,279 --> 00:23:29,039
+validating whether it does a good job at
+
+580
+00:23:27,279 --> 00:23:33,080
+evaluation and
+
+581
+00:23:29,039 --> 00:23:34,919
+and my the results are all across the
+
+582
+00:23:33,080 --> 00:23:36,880
+board it can be anywhere from very very
+
+583
+00:23:34,919 --> 00:23:38,640
+good to very very bad at evaluating
+
+584
+00:23:36,880 --> 00:23:41,320
+particular tasks so I would be at least
+
+585
+00:23:38,640 --> 00:23:43,559
+a little bit suspicious of whether gp4
+
+586
+00:23:41,320 --> 00:23:45,679
+is doing a good job evaluating for your
+
+587
+00:23:43,559 --> 00:23:49,320
+task especially more complex
+
+588
+00:23:45,679 --> 00:23:51,960
+tests um I would especially be
+
+589
+00:23:49,320 --> 00:23:54,000
+suspicious if you're doing two uh any of
+
+590
+00:23:51,960 --> 00:23:56,760
+the two following things number one if
+
+591
+00:23:54,000 --> 00:23:59,880
+you're comparing gp4 or any model
+
+592
+00:23:56,760 --> 00:24:02,400
+against itself in another model because
+
+593
+00:23:59,880 --> 00:24:05,200
+gp4 really likes
+
+594
+00:24:02,400 --> 00:24:06,880
+gp4 it really likes its own outputs and
+
+595
+00:24:05,200 --> 00:24:08,120
+there are papers uh sorry I don't
+
+596
+00:24:06,880 --> 00:24:09,679
+actually have the references here but I
+
+597
+00:24:08,120 --> 00:24:11,200
+can follow up if people are interested
+
+598
+00:24:09,679 --> 00:24:13,080
+but there are papers that demonstrate
+
+599
+00:24:11,200 --> 00:24:15,799
+that gp4 likes it you know its own
+
+600
+00:24:13,080 --> 00:24:19,200
+outputs more than others also if you're
+
+601
+00:24:15,799 --> 00:24:22,120
+explicitly optimizing the outputs using
+
+602
+00:24:19,200 --> 00:24:24,640
+rlf um there is something called good
+
+603
+00:24:22,120 --> 00:24:27,120
+Hearts law which is basically anytime
+
+604
+00:24:24,640 --> 00:24:29,520
+you uh start optimizing towards a metric
+
+605
+00:24:27,120 --> 00:24:32,559
+it becomes a bad metric and that also
+
+606
+00:24:29,520 --> 00:24:35,000
+happens for gp4 based evaluations so if
+
+607
+00:24:32,559 --> 00:24:37,200
+you start optimizing for gp4 based
+
+608
+00:24:35,000 --> 00:24:38,960
+evaluations especially for reference
+
+609
+00:24:37,200 --> 00:24:41,679
+list metrics that don't use a reference
+
+610
+00:24:38,960 --> 00:24:44,840
+output then um you start basically
+
+611
+00:24:41,679 --> 00:24:47,440
+exploiting the metric
+
+612
+00:24:44,840 --> 00:24:49,840
+um another thing that you can do with QA
+
+613
+00:24:47,440 --> 00:24:53,279
+based evaluation is ask about fine grade
+
+614
+00:24:49,840 --> 00:24:54,919
+mistakes and so this is a paper by um uh
+
+615
+00:24:53,279 --> 00:24:56,480
+Patrick Fernandez who's a student who's
+
+616
+00:24:54,919 --> 00:25:02,080
+working with me and basically what we
+
+617
+00:24:56,480 --> 00:25:05,240
+did is we asked the model to um not give
+
+618
+00:25:02,080 --> 00:25:07,360
+a particular score but actually identify
+
+619
+00:25:05,240 --> 00:25:08,880
+the mistakes in the output and when we
+
+620
+00:25:07,360 --> 00:25:10,559
+asked it to identify the mistakes in the
+
+621
+00:25:08,880 --> 00:25:13,720
+output we found that this gave more
+
+622
+00:25:10,559 --> 00:25:17,320
+consistent uh results so kind of
+
+623
+00:25:13,720 --> 00:25:18,840
+interestingly we ask humans to identify
+
+624
+00:25:17,320 --> 00:25:21,120
+individual mistakes and the output that
+
+625
+00:25:18,840 --> 00:25:24,240
+gives humans more consistent results
+
+626
+00:25:21,120 --> 00:25:25,559
+it's the same thing for gp4 so um that
+
+627
+00:25:24,240 --> 00:25:27,320
+that's another paper you can look at if
+
+628
+00:25:25,559 --> 00:25:29,640
+you're
+
+629
+00:25:27,320 --> 00:25:32,679
+interested
+
+630
+00:25:29,640 --> 00:25:38,000
+cool um so I I mentioned that you could
+
+631
+00:25:32,679 --> 00:25:38,000
+or could not uh trust uh yeah sorry go
+
+632
+00:25:44,679 --> 00:25:51,279
+ahead uh correct so yeah B basically
+
+633
+00:25:47,360 --> 00:25:53,279
+just what you do is you have the source
+
+634
+00:25:51,279 --> 00:25:54,960
+um ideally you'll also have a reference
+
+635
+00:25:53,279 --> 00:25:57,840
+output that was created by skilled
+
+636
+00:25:54,960 --> 00:25:59,720
+humans and then you put in the Target
+
+637
+00:25:57,840 --> 00:26:02,279
+you know output basically you have the
+
+638
+00:25:59,720 --> 00:26:08,000
+input ideally a reference output created
+
+639
+00:26:02,279 --> 00:26:08,000
+by Good by skilled humans and uh like
+
+640
+00:26:15,159 --> 00:26:20,240
+hypothesis yeah I
+
+641
+00:26:17,919 --> 00:26:24,559
+mean it's a good question and I don't
+
+642
+00:26:20,240 --> 00:26:26,919
+know if we actually have a a very clear
+
+643
+00:26:24,559 --> 00:26:31,399
+empirical like evidence of why this is
+
+644
+00:26:26,919 --> 00:26:33,320
+the case but my hypothesis about this is
+
+645
+00:26:31,399 --> 00:26:36,159
+yes we kind of would expect models to be
+
+646
+00:26:33,320 --> 00:26:38,200
+more biased towards their own outputs
+
+647
+00:26:36,159 --> 00:26:40,919
+and the reason why is because
+
+648
+00:26:38,200 --> 00:26:43,080
+essentially you know models
+
+649
+00:26:40,919 --> 00:26:44,279
+are within their embeddings they're
+
+650
+00:26:43,080 --> 00:26:45,760
+encoding when they're in a high
+
+651
+00:26:44,279 --> 00:26:47,600
+probability part of the space and when
+
+652
+00:26:45,760 --> 00:26:50,200
+they're in a low probability part of the
+
+653
+00:26:47,600 --> 00:26:51,120
+space and like the high probability part
+
+654
+00:26:50,200 --> 00:26:54,600
+of the
+
+655
+00:26:51,120 --> 00:26:56,200
+space is going to be the high
+
+656
+00:26:54,600 --> 00:26:58,600
+probability part of the space is going
+
+657
+00:26:56,200 --> 00:27:02,559
+to be associated with good outputs
+
+658
+00:26:58,600 --> 00:27:07,000
+because like when
+
+659
+00:27:02,559 --> 00:27:08,600
+models are more sure of their outputs
+
+660
+00:27:07,000 --> 00:27:11,960
+they're more likely to be
+
+661
+00:27:08,600 --> 00:27:13,520
+good just because that indicates that
+
+662
+00:27:11,960 --> 00:27:15,240
+like they're closer to the training data
+
+663
+00:27:13,520 --> 00:27:17,760
+that it had and other things like that
+
+664
+00:27:15,240 --> 00:27:21,600
+so model probabilities are associated
+
+665
+00:27:17,760 --> 00:27:23,760
+with outputs uh with uh with good
+
+666
+00:27:21,600 --> 00:27:26,600
+outputs but just
+
+667
+00:27:23,760 --> 00:27:29,440
+correla separately from
+
+668
+00:27:26,600 --> 00:27:32,120
+that I believe a model can identify when
+
+669
+00:27:29,440 --> 00:27:33,320
+it's in a high probability segment of
+
+670
+00:27:32,120 --> 00:27:35,799
+the space and when it's in a low
+
+671
+00:27:33,320 --> 00:27:39,399
+probability segment of the space and
+
+672
+00:27:35,799 --> 00:27:39,399
+because of that I expect
+
+673
+00:27:39,519 --> 00:27:45,519
+that I like there are segments of the
+
+674
+00:27:43,240 --> 00:27:47,120
+embedding space where it's more likely
+
+675
+00:27:45,519 --> 00:27:48,360
+to answer yes about something being good
+
+676
+00:27:47,120 --> 00:27:50,960
+or not and those are going to be
+
+677
+00:27:48,360 --> 00:27:54,760
+associated with high uh like high
+
+678
+00:27:50,960 --> 00:27:56,159
+probability outbreaks as well and also
+
+679
+00:27:54,760 --> 00:27:57,760
+models are more likely to generate
+
+680
+00:27:56,159 --> 00:28:00,240
+outputs that are high probability
+
+681
+00:27:57,760 --> 00:28:02,320
+according into their model by definition
+
+682
+00:28:00,240 --> 00:28:03,880
+so all three of those effects together
+
+683
+00:28:02,320 --> 00:28:05,640
+would basically go into a model being
+
+684
+00:28:03,880 --> 00:28:09,120
+bios supports its own outputs compared
+
+685
+00:28:05,640 --> 00:28:11,559
+to that puts in another model but um
+
+686
+00:28:09,120 --> 00:28:13,279
+yeah this is a very handwavy explanation
+
+687
+00:28:11,559 --> 00:28:15,519
+but like putting the two the three
+
+688
+00:28:13,279 --> 00:28:18,600
+together models output high probability
+
+689
+00:28:15,519 --> 00:28:20,880
+things from their own probability Space
+
+690
+00:28:18,600 --> 00:28:23,440
+by definition
+
+691
+00:28:20,880 --> 00:28:25,760
+um things that are high probability are
+
+692
+00:28:23,440 --> 00:28:27,519
+associated with being good uh just
+
+693
+00:28:25,760 --> 00:28:29,279
+because otherwise a model would be
+
+694
+00:28:27,519 --> 00:28:31,840
+outputting garbage
+
+695
+00:28:29,279 --> 00:28:33,840
+and um the final thing which is more
+
+696
+00:28:31,840 --> 00:28:35,679
+tenuous is if the model is in a high
+
+697
+00:28:33,840 --> 00:28:37,919
+probability segment of the space it's
+
+698
+00:28:35,679 --> 00:28:39,760
+more likely to Output yes according to a
+
+699
+00:28:37,919 --> 00:28:41,480
+question of it being good and I I think
+
+700
+00:28:39,760 --> 00:28:44,360
+that's probably true but I'm not 100%
+
+701
+00:28:41,480 --> 00:28:44,360
+sure about the the
+
+702
+00:28:45,559 --> 00:28:51,039
+fin um maybe maybe someone wants to
+
+703
+00:28:49,000 --> 00:28:52,840
+examinate examine that as a final
+
+704
+00:28:51,039 --> 00:28:54,200
+project it seems like a interesting
+
+705
+00:28:52,840 --> 00:28:57,080
+interesting
+
+706
+00:28:54,200 --> 00:29:00,039
+question um cool uh were there any other
+
+707
+00:28:57,080 --> 00:29:00,039
+questions about these methods
+
+708
+00:29:00,159 --> 00:29:07,120
+here um okay so when I say like an
+
+709
+00:29:03,960 --> 00:29:11,080
+evaluation metric is good or not what do
+
+710
+00:29:07,120 --> 00:29:13,200
+I mean by this being good or not um or a
+
+711
+00:29:11,080 --> 00:29:16,880
+reward model or whatever else and
+
+712
+00:29:13,200 --> 00:29:18,440
+basically the um the way we typically do
+
+713
+00:29:16,880 --> 00:29:19,840
+this is by doing something called meta
+
+714
+00:29:18,440 --> 00:29:22,440
+evaluation so it's called meta
+
+715
+00:29:19,840 --> 00:29:25,799
+evaluation because it's evaluation of
+
+716
+00:29:22,440 --> 00:29:29,279
+evaluation and uh the way we do this is
+
+717
+00:29:25,799 --> 00:29:32,519
+we have human uh scores and we have
+
+718
+00:29:29,279 --> 00:29:34,760
+automatic scores and we usually
+
+719
+00:29:32,519 --> 00:29:38,640
+calculate some sort of correlation
+
+720
+00:29:34,760 --> 00:29:41,000
+between the scores so um typical ones
+
+721
+00:29:38,640 --> 00:29:46,440
+are rank correlations like Pearson's
+
+722
+00:29:41,000 --> 00:29:48,799
+correlation or tendle uh Tow and uh so
+
+723
+00:29:46,440 --> 00:29:51,200
+the more Associated the automatic scores
+
+724
+00:29:48,799 --> 00:29:53,960
+are with the human scores the higher
+
+725
+00:29:51,200 --> 00:29:55,159
+these correlations are going to be um
+
+726
+00:29:53,960 --> 00:29:57,559
+there's other things that you can
+
+727
+00:29:55,159 --> 00:30:00,080
+calculate so if you're trying to figure
+
+728
+00:29:57,559 --> 00:30:01,640
+out whether a model um matches human
+
+729
+00:30:00,080 --> 00:30:04,279
+pairwise preferences you can just
+
+730
+00:30:01,640 --> 00:30:06,440
+calculate accuracy so I didn't put that
+
+731
+00:30:04,279 --> 00:30:08,080
+on um I didn't put that on the slide
+
+732
+00:30:06,440 --> 00:30:10,880
+here but you can just calculate accuracy
+
+733
+00:30:08,080 --> 00:30:13,120
+of pairwise preferences um you can also
+
+734
+00:30:10,880 --> 00:30:15,360
+calculate the absolute error between the
+
+735
+00:30:13,120 --> 00:30:19,320
+the judgments if you want to know uh
+
+736
+00:30:15,360 --> 00:30:21,720
+whether the absolute error matches so um
+
+737
+00:30:19,320 --> 00:30:24,159
+the these are good things to do if you
+
+738
+00:30:21,720 --> 00:30:25,600
+want to use an evaluation metric but you
+
+739
+00:30:24,159 --> 00:30:27,200
+aren't sure whether it's good or not I
+
+740
+00:30:25,600 --> 00:30:29,640
+would check to see whether the authors
+
+741
+00:30:27,200 --> 00:30:32,000
+have done this sort of meta evaluation
+
+742
+00:30:29,640 --> 00:30:33,760
+if they haven't be a little bit
+
+743
+00:30:32,000 --> 00:30:36,960
+suspicious if they have be a little bit
+
+744
+00:30:33,760 --> 00:30:39,799
+less suspicious but um
+
+745
+00:30:36,960 --> 00:30:42,960
+yeah how do people do this typically uh
+
+746
+00:30:39,799 --> 00:30:45,640
+usually they create uh data sets like
+
+747
+00:30:42,960 --> 00:30:49,440
+the WM they use data sets like the WMT
+
+748
+00:30:45,640 --> 00:30:53,960
+shared tasks um or
+
+749
+00:30:49,440 --> 00:30:57,679
+uh uh like some evl um but there's also
+
+750
+00:30:53,960 --> 00:30:59,960
+other ways to create um uh there's also
+
+751
+00:30:57,679 --> 00:31:01,639
+Lots other data sets but in order to do
+
+752
+00:30:59,960 --> 00:31:05,639
+this reliably you need a fairly large
+
+753
+00:31:01,639 --> 00:31:05,639
+data set so it's one thing to be aware
+
+754
+00:31:07,080 --> 00:31:10,760
+of
+
+755
+00:31:08,720 --> 00:31:14,200
+cool
+
+756
+00:31:10,760 --> 00:31:16,360
+um then the final thing um all of the
+
+757
+00:31:14,200 --> 00:31:17,919
+automatic evaluation methods that I
+
+758
+00:31:16,360 --> 00:31:20,240
+talked about now are trying to match
+
+759
+00:31:17,919 --> 00:31:22,679
+human preferences but that's not the
+
+760
+00:31:20,240 --> 00:31:24,960
+only thing that you necessarily want to
+
+761
+00:31:22,679 --> 00:31:28,440
+do the final thing that you might want
+
+762
+00:31:24,960 --> 00:31:30,840
+to do is uh use the model outputs in a
+
+763
+00:31:28,440 --> 00:31:34,200
+downstream system and see whether they
+
+764
+00:31:30,840 --> 00:31:36,399
+are effective for that so there's two
+
+765
+00:31:34,200 --> 00:31:39,080
+concepts of intrinsic evaluation and
+
+766
+00:31:36,399 --> 00:31:41,720
+extrinsic evaluation so intrinsic
+
+767
+00:31:39,080 --> 00:31:44,159
+evaluation um evaluates the quality of
+
+768
+00:31:41,720 --> 00:31:45,720
+the output itself and so that would be
+
+769
+00:31:44,159 --> 00:31:48,639
+like asking a human directly about how
+
+770
+00:31:45,720 --> 00:31:50,720
+good is this output extrinsic evaluation
+
+771
+00:31:48,639 --> 00:31:53,679
+is evaluating output quality by its
+
+772
+00:31:50,720 --> 00:31:57,000
+utility um and so just to give one
+
+773
+00:31:53,679 --> 00:31:58,360
+example um if you can evaluate large
+
+774
+00:31:57,000 --> 00:32:00,200
+language model summary
+
+775
+00:31:58,360 --> 00:32:04,200
+through question answering
+
+776
+00:32:00,200 --> 00:32:05,880
+accuracy um and so you can take the
+
+777
+00:32:04,200 --> 00:32:07,399
+output of an llm and feed it through a
+
+778
+00:32:05,880 --> 00:32:09,600
+question answering model and see whether
+
+779
+00:32:07,399 --> 00:32:12,399
+you're able to answer questions based on
+
+780
+00:32:09,600 --> 00:32:15,799
+this and that kind of gives you a better
+
+781
+00:32:12,399 --> 00:32:18,279
+idea of whether the summary require uh
+
+782
+00:32:15,799 --> 00:32:20,120
+incorporates requisite information but
+
+783
+00:32:18,279 --> 00:32:22,120
+if you think about anything an llm can
+
+784
+00:32:20,120 --> 00:32:23,760
+be used for usually it's part of a
+
+785
+00:32:22,120 --> 00:32:26,679
+bigger system so you can evaluate it as
+
+786
+00:32:23,760 --> 00:32:28,399
+a part of that bigger system um the
+
+787
+00:32:26,679 --> 00:32:30,639
+problem with this is it's a very
+
+788
+00:32:28,399 --> 00:32:33,960
+indirect way of assessing things so like
+
+789
+00:32:30,639 --> 00:32:36,080
+let's say your QA model is just bad uh
+
+790
+00:32:33,960 --> 00:32:38,480
+how can you disentangle the effect of
+
+791
+00:32:36,080 --> 00:32:41,679
+the L summary versus the QA model that's
+
+792
+00:32:38,480 --> 00:32:44,120
+not a trivial thing to do so ideally
+
+793
+00:32:41,679 --> 00:32:47,000
+like a combination of these two is
+
+794
+00:32:44,120 --> 00:32:47,000
+practically the best way
+
+795
+00:32:48,039 --> 00:32:52,200
+go cool so
+
+796
+00:32:56,039 --> 00:32:59,960
+yeah yeah it wouldn't necessar
+
+797
+00:32:58,360 --> 00:33:05,679
+say it's harder to do it might even be
+
+798
+00:32:59,960 --> 00:33:05,679
+easier to do um which is like let's
+
+799
+00:33:06,679 --> 00:33:11,720
+say Let me let me see if I can come up
+
+800
+00:33:09,360 --> 00:33:11,720
+with
+
+801
+00:33:12,639 --> 00:33:17,600
+example what let's
+
+802
+00:33:15,000 --> 00:33:19,670
+say you
+
+803
+00:33:17,600 --> 00:33:22,979
+are trying
+
+804
+00:33:19,670 --> 00:33:22,979
+[Music]
+
+805
+00:33:24,639 --> 00:33:29,760
+to let's say you're trying to
+
+806
+00:33:30,559 --> 00:33:33,559
+guess
+
+807
+00:33:39,000 --> 00:33:45,399
+whether let's say you're trying to guess
+
+808
+00:33:42,399 --> 00:33:46,559
+whether a someone will be hired at a
+
+809
+00:33:45,399 --> 00:33:52,039
+company or
+
+810
+00:33:46,559 --> 00:33:53,880
+not based on an llm generated summary of
+
+811
+00:33:52,039 --> 00:33:58,880
+their qualifications for a position or
+
+812
+00:33:53,880 --> 00:34:01,799
+something like that um and
+
+813
+00:33:58,880 --> 00:34:03,080
+you what actually maybe this is not a
+
+814
+00:34:01,799 --> 00:34:04,720
+great example because whether you should
+
+815
+00:34:03,080 --> 00:34:06,960
+be doing this ethically is a little bit
+
+816
+00:34:04,720 --> 00:34:08,159
+unclear but let's say you were doing
+
+817
+00:34:06,960 --> 00:34:09,560
+let's say you were doing something like
+
+818
+00:34:08,159 --> 00:34:11,520
+that just because it's one example I can
+
+819
+00:34:09,560 --> 00:34:14,320
+think of right now whether they will get
+
+820
+00:34:11,520 --> 00:34:16,320
+hired or not is um is clear because you
+
+821
+00:34:14,320 --> 00:34:19,399
+have a objective answer right whether
+
+822
+00:34:16,320 --> 00:34:21,480
+they were hired or not um or maybe maybe
+
+823
+00:34:19,399 --> 00:34:23,800
+another example would be like let's say
+
+824
+00:34:21,480 --> 00:34:26,320
+um let's say you want to predict the
+
+825
+00:34:23,800 --> 00:34:29,599
+diagnosis in a medical application based
+
+826
+00:34:26,320 --> 00:34:32,960
+on an llm generated some of somebody's
+
+827
+00:34:29,599 --> 00:34:35,919
+uh you know LM generated summary of
+
+828
+00:34:32,960 --> 00:34:38,480
+somebody's you know past medical history
+
+829
+00:34:35,919 --> 00:34:40,839
+and all this stuff and here you want the
+
+830
+00:34:38,480 --> 00:34:43,440
+llm generated summary you definitely
+
+831
+00:34:40,839 --> 00:34:44,879
+want the summary because the summary is
+
+832
+00:34:43,440 --> 00:34:47,560
+going to be viewed by a doctor who will
+
+833
+00:34:44,879 --> 00:34:49,359
+make the final decision but you also
+
+834
+00:34:47,560 --> 00:34:50,760
+have information about the diagnoses of
+
+835
+00:34:49,359 --> 00:34:52,399
+all the people in your medical system
+
+836
+00:34:50,760 --> 00:34:54,560
+later because you know they went through
+
+837
+00:34:52,399 --> 00:34:56,480
+your medical system for years and you
+
+838
+00:34:54,560 --> 00:34:58,200
+know later like through lots of tests
+
+839
+00:34:56,480 --> 00:35:00,800
+and stuff uh whether how they were
+
+840
+00:34:58,200 --> 00:35:02,320
+diagnosed so you generate an LM based
+
+841
+00:35:00,800 --> 00:35:05,000
+summary and then you predict the
+
+842
+00:35:02,320 --> 00:35:06,599
+diagnosis from the summary so there the
+
+843
+00:35:05,000 --> 00:35:08,040
+evaluation of the diagnosis is very
+
+844
+00:35:06,599 --> 00:35:11,480
+clear because you kind of have a gold
+
+845
+00:35:08,040 --> 00:35:12,599
+standard answer um but the EV intrinsic
+
+846
+00:35:11,480 --> 00:35:14,839
+evaluation of whether it's a good
+
+847
+00:35:12,599 --> 00:35:16,839
+summary or not is not as clear because
+
+848
+00:35:14,839 --> 00:35:19,400
+you'd have pass do whether it's good and
+
+849
+00:35:16,839 --> 00:35:21,079
+understandable summary so the extrinsic
+
+850
+00:35:19,400 --> 00:35:24,920
+evaluation might be easier because it's
+
+851
+00:35:21,079 --> 00:35:26,480
+clearer um so there are cases like that
+
+852
+00:35:24,920 --> 00:35:30,720
+um the problem is you would have to have
+
+853
+00:35:26,480 --> 00:35:33,800
+that data in order to do that um yeah do
+
+854
+00:35:30,720 --> 00:35:38,240
+like evaluation yeah I was just
+
+855
+00:35:33,800 --> 00:35:40,800
+wondering typically the
+
+856
+00:35:38,240 --> 00:35:42,880
+like like how do you accomodate the
+
+857
+00:35:40,800 --> 00:35:47,160
+diversity oh yeah that's a great that's
+
+858
+00:35:42,880 --> 00:35:50,240
+a great question um so how do you how do
+
+859
+00:35:47,160 --> 00:35:50,240
+you get these scores
+
+860
+00:35:50,720 --> 00:35:55,800
+here there's a number of different
+
+861
+00:35:53,200 --> 00:35:59,160
+things in the WMT shared tasks what they
+
+862
+00:35:55,800 --> 00:36:00,280
+did is they did
+
+863
+00:35:59,160 --> 00:36:03,200
+the first thing they do is they
+
+864
+00:36:00,280 --> 00:36:06,319
+normalize by annotator and what they do
+
+865
+00:36:03,200 --> 00:36:10,400
+is they basically take the zcore or Z
+
+866
+00:36:06,319 --> 00:36:12,240
+score of the um of the human annotator's
+
+867
+00:36:10,400 --> 00:36:14,880
+actual scores because some people are
+
+868
+00:36:12,240 --> 00:36:16,400
+more harsh than other people and so what
+
+869
+00:36:14,880 --> 00:36:20,680
+that means is you basically normalize to
+
+870
+00:36:16,400 --> 00:36:22,119
+have zero mean in unit variance um and
+
+871
+00:36:20,680 --> 00:36:24,119
+then after they've normalized to zero
+
+872
+00:36:22,119 --> 00:36:29,560
+mean and unit variance then I think they
+
+873
+00:36:24,119 --> 00:36:29,560
+average together different humans so um
+
+874
+00:36:30,160 --> 00:36:36,520
+then for how do you deal with the fact
+
+875
+00:36:33,680 --> 00:36:38,040
+that humans disagree on things and I
+
+876
+00:36:36,520 --> 00:36:39,480
+think it's pretty varied I don't know if
+
+877
+00:36:38,040 --> 00:36:42,160
+there's any gold standard way of doing
+
+878
+00:36:39,480 --> 00:36:43,839
+it but sometimes you just average
+
+879
+00:36:42,160 --> 00:36:46,359
+sometimes you throw away examples where
+
+880
+00:36:43,839 --> 00:36:47,960
+humans disagree a lot um because like
+
+881
+00:36:46,359 --> 00:36:50,200
+you can't get the humans to agree how
+
+882
+00:36:47,960 --> 00:36:53,319
+could you expect how could you expect a
+
+883
+00:36:50,200 --> 00:36:55,119
+machine to do well um so I think it it's
+
+884
+00:36:53,319 --> 00:36:59,200
+a little bit test
+
+885
+00:36:55,119 --> 00:37:01,560
+defending yeah so for
+
+886
+00:36:59,200 --> 00:37:04,560
+generation inin
+
+887
+00:37:01,560 --> 00:37:06,280
+andin yeah so for code generation that's
+
+888
+00:37:04,560 --> 00:37:08,200
+I I I love this example because I've
+
+889
+00:37:06,280 --> 00:37:09,960
+worked on code generation a lot of
+
+890
+00:37:08,200 --> 00:37:12,680
+people only think about extrinsic
+
+891
+00:37:09,960 --> 00:37:14,400
+evaluation of code Generation Um or I
+
+892
+00:37:12,680 --> 00:37:16,160
+don't know if it's extrinsic but only
+
+893
+00:37:14,400 --> 00:37:19,160
+think about execution based evaluation
+
+894
+00:37:16,160 --> 00:37:20,520
+of code generation which is like you
+
+895
+00:37:19,160 --> 00:37:22,400
+execute the code you see whether it
+
+896
+00:37:20,520 --> 00:37:25,040
+passs unit tests and other things like
+
+897
+00:37:22,400 --> 00:37:26,839
+this but in reality actually there's a
+
+898
+00:37:25,040 --> 00:37:28,599
+lot of other important things for code
+
+899
+00:37:26,839 --> 00:37:30,560
+like readability and other stuff like
+
+900
+00:37:28,599 --> 00:37:32,160
+that and you should be evaluating those
+
+901
+00:37:30,560 --> 00:37:34,920
+things but I think a lot of people like
+
+902
+00:37:32,160 --> 00:37:36,520
+kind of ignore that so um there there
+
+903
+00:37:34,920 --> 00:37:38,880
+are a few Pap that do that but most of
+
+904
+00:37:36,520 --> 00:37:41,000
+the time people just execute the Cod
+
+905
+00:37:38,880 --> 00:37:45,520
+process
+
+906
+00:37:41,000 --> 00:37:47,760
+un cool okay um so yeah moving on to the
+
+907
+00:37:45,520 --> 00:37:51,160
+learning part so now I'd like to talk
+
+908
+00:37:47,760 --> 00:37:55,280
+about uh learning and the first thing
+
+909
+00:37:51,160 --> 00:37:59,480
+I'll cover is error and risk and so
+
+910
+00:37:55,280 --> 00:38:02,280
+basically um the way we calculate air is
+
+911
+00:37:59,480 --> 00:38:03,119
+we generate an output and we calculate
+
+912
+00:38:02,280 --> 00:38:07,680
+its
+
+913
+00:38:03,119 --> 00:38:09,480
+Badness um and so generating the output
+
+914
+00:38:07,680 --> 00:38:13,160
+could be argmax it could be sampling it
+
+915
+00:38:09,480 --> 00:38:15,800
+could be anything else like that um and
+
+916
+00:38:13,160 --> 00:38:18,640
+we calculate its Badness uh which is one
+
+917
+00:38:15,800 --> 00:38:21,040
+minus in which could be like how bad is
+
+918
+00:38:18,640 --> 00:38:22,720
+the output uh if you're you have a
+
+919
+00:38:21,040 --> 00:38:24,760
+Badness measure or it could be one minus
+
+920
+00:38:22,720 --> 00:38:28,400
+the evaluation Square to calculate its
+
+921
+00:38:24,760 --> 00:38:30,160
+Badness and this is defined as error
+
+922
+00:38:28,400 --> 00:38:31,440
+and generally what you want to do is you
+
+923
+00:38:30,160 --> 00:38:33,520
+want to minimize
+
+924
+00:38:31,440 --> 00:38:36,800
+error
+
+925
+00:38:33,520 --> 00:38:39,400
+um because in the end you're going to be
+
+926
+00:38:36,800 --> 00:38:42,359
+deploying A system that just outputs you
+
+927
+00:38:39,400 --> 00:38:46,079
+know one thing and uh you're going to
+
+928
+00:38:42,359 --> 00:38:49,800
+want that to be as good a thing as
+
+929
+00:38:46,079 --> 00:38:53,000
+possible um but the problem with this is
+
+930
+00:38:49,800 --> 00:38:56,400
+there's no easy way to actually optimize
+
+931
+00:38:53,000 --> 00:38:59,079
+this value in especially in a text
+
+932
+00:38:56,400 --> 00:39:01,800
+generation sty setting but even in the
+
+933
+00:38:59,079 --> 00:39:06,839
+classification setting we can't easily
+
+934
+00:39:01,800 --> 00:39:06,839
+maximize err because um if you look at
+
+935
+00:39:09,040 --> 00:39:14,200
+the if you look at the surface of air uh
+
+936
+00:39:12,760 --> 00:39:15,960
+at some point you're going to have a
+
+937
+00:39:14,200 --> 00:39:18,319
+non-differentiable part when you take
+
+938
+00:39:15,960 --> 00:39:21,119
+the argmax and or when you do sampling
+
+939
+00:39:18,319 --> 00:39:23,319
+or anything like that so um you're not
+
+940
+00:39:21,119 --> 00:39:27,119
+going to be able to do gradient based
+
+941
+00:39:23,319 --> 00:39:29,200
+optimization so what we do normally is
+
+942
+00:39:27,119 --> 00:39:33,400
+um
+
+943
+00:39:29,200 --> 00:39:37,000
+we instead calculate something uh called
+
+944
+00:39:33,400 --> 00:39:38,560
+risk and what risk looks like is uh we
+
+945
+00:39:37,000 --> 00:39:40,599
+talked a little bit about minimum based
+
+946
+00:39:38,560 --> 00:39:43,520
+risk for decoding but this is for uh
+
+947
+00:39:40,599 --> 00:39:46,160
+training time and what it looks like is
+
+948
+00:39:43,520 --> 00:39:49,040
+it's essentially the expected err of the
+
+949
+00:39:46,160 --> 00:39:52,359
+output and the expected err of the
+
+950
+00:39:49,040 --> 00:39:54,760
+output um includes a probability in the
+
+951
+00:39:52,359 --> 00:39:58,240
+objective function here and that
+
+952
+00:39:54,760 --> 00:40:01,079
+probability uh is differential basically
+
+953
+00:39:58,240 --> 00:40:02,319
+so we can um uh we can easily do
+
+954
+00:40:01,079 --> 00:40:05,720
+gradient based
+
+955
+00:40:02,319 --> 00:40:09,119
+optimization through it um the problem
+
+956
+00:40:05,720 --> 00:40:12,200
+with this is It's differentiable but for
+
+957
+00:40:09,119 --> 00:40:17,160
+text generation for example the sum is
+
+958
+00:40:12,200 --> 00:40:20,319
+intractable because we have a combinator
+
+959
+00:40:17,160 --> 00:40:23,880
+large number of potential outputs um
+
+960
+00:40:20,319 --> 00:40:25,520
+because you know if this is we've talked
+
+961
+00:40:23,880 --> 00:40:28,720
+about this before but if this is like
+
+962
+00:40:25,520 --> 00:40:30,680
+link you know 50 and we have a 30,000
+
+963
+00:40:28,720 --> 00:40:32,839
+vocabul that's 30,000 to the 50
+
+964
+00:40:30,680 --> 00:40:34,599
+possibilities we can't take a su over
+
+965
+00:40:32,839 --> 00:40:36,359
+that many
+
+966
+00:40:34,599 --> 00:40:38,400
+possibilities
+
+967
+00:40:36,359 --> 00:40:42,680
+um
+
+968
+00:40:38,400 --> 00:40:45,839
+so minimum R risk training uh tries to
+
+969
+00:40:42,680 --> 00:40:48,440
+minimize risk reinforcement learning
+
+970
+00:40:45,839 --> 00:40:50,040
+also many of the models especially
+
+971
+00:40:48,440 --> 00:40:53,599
+policy gradient models are trying to
+
+972
+00:40:50,040 --> 00:40:55,240
+minimize risk as well so um but the
+
+973
+00:40:53,599 --> 00:40:58,040
+reason why I wanted to talk about risk
+
+974
+00:40:55,240 --> 00:41:00,440
+first is because this is very simple to
+
+975
+00:40:58,040 --> 00:41:01,640
+get to from the uh the point of view of
+
+976
+00:41:00,440 --> 00:41:06,560
+like all the things that we've studied
+
+977
+00:41:01,640 --> 00:41:06,560
+so so I think it's talking about
+
+978
+00:41:06,760 --> 00:41:11,800
+that
+
+979
+00:41:08,319 --> 00:41:15,520
+um one other thing that I should mention
+
+980
+00:41:11,800 --> 00:41:18,400
+about is
+
+981
+00:41:15,520 --> 00:41:23,079
+um or no sorry I'll I'll talk about that
+
+982
+00:41:18,400 --> 00:41:26,880
+later so when we want to optimize risk
+
+983
+00:41:23,079 --> 00:41:30,560
+um what we do is we sample in order to
+
+984
+00:41:26,880 --> 00:41:35,520
+make this trct so a very simple way to
+
+985
+00:41:30,560 --> 00:41:37,640
+minimize risk is instead of um instead
+
+986
+00:41:35,520 --> 00:41:39,359
+of summing over all of the possible
+
+987
+00:41:37,640 --> 00:41:42,760
+outputs we sum over a small number of
+
+988
+00:41:39,359 --> 00:41:46,079
+possible outputs and we upgrade uh and
+
+989
+00:41:42,760 --> 00:41:47,359
+we uh sorry normalize uh to make this
+
+990
+00:41:46,079 --> 00:41:51,200
+all add up to
+
+991
+00:41:47,359 --> 00:41:52,839
+one and so this normalizer here is
+
+992
+00:41:51,200 --> 00:41:55,319
+basically the sum over all of the
+
+993
+00:41:52,839 --> 00:41:58,599
+probabilities that we have uh on the top
+
+994
+00:41:55,319 --> 00:42:02,119
+part here and and these samples can be
+
+995
+00:41:58,599 --> 00:42:05,480
+created either using sampling or n best
+
+996
+00:42:02,119 --> 00:42:07,040
+search we don't need to have from the
+
+997
+00:42:05,480 --> 00:42:11,040
+point of view of doing this sort of
+
+998
+00:42:07,040 --> 00:42:13,960
+minimum risk training the kind of
+
+999
+00:42:11,040 --> 00:42:16,880
+correct way of doing this is sampling
+
+1000
+00:42:13,960 --> 00:42:19,880
+using ancestral sampling uh like we
+
+1001
+00:42:16,880 --> 00:42:23,079
+talked about before and um in minimizing
+
+1002
+00:42:19,880 --> 00:42:25,839
+the output based on the the samples but
+
+1003
+00:42:23,079 --> 00:42:28,480
+the problem with that is um as many of
+
+1004
+00:42:25,839 --> 00:42:31,440
+you also might have seen when you were
+
+1005
+00:42:28,480 --> 00:42:33,599
+sampling from your language model uh
+
+1006
+00:42:31,440 --> 00:42:35,160
+from assignment one if you sample with
+
+1007
+00:42:33,599 --> 00:42:38,040
+temperature one it gives you a lot of
+
+1008
+00:42:35,160 --> 00:42:40,720
+like not very good outlets right and so
+
+1009
+00:42:38,040 --> 00:42:43,400
+if you're sampling with temperature one
+
+1010
+00:42:40,720 --> 00:42:45,000
+um you'll be exploring a a very large
+
+1011
+00:42:43,400 --> 00:42:47,880
+part of the space that actually isn't
+
+1012
+00:42:45,000 --> 00:42:49,720
+very good and so because of this uh some
+
+1013
+00:42:47,880 --> 00:42:51,480
+other Alternatives that you can use is
+
+1014
+00:42:49,720 --> 00:42:53,400
+you can just do endb search to find the
+
+1015
+00:42:51,480 --> 00:42:55,280
+best outputs or you can sample with a
+
+1016
+00:42:53,400 --> 00:42:58,079
+temperature that's not one or something
+
+1017
+00:42:55,280 --> 00:43:00,240
+like that and basically create uh you
+
+1018
+00:42:58,079 --> 00:43:02,520
+know a list of possible hypotheses and
+
+1019
+00:43:00,240 --> 00:43:04,079
+then normalize other B so that's another
+
+1020
+00:43:02,520 --> 00:43:06,240
+option and very often not using
+
+1021
+00:43:04,079 --> 00:43:11,200
+temperature one is a better
+
+1022
+00:43:06,240 --> 00:43:15,280
+way um if you're sampling with not
+
+1023
+00:43:11,200 --> 00:43:18,640
+temperature one and you are um
+
+1024
+00:43:15,280 --> 00:43:20,920
+potentially getting multiple outputs you
+
+1025
+00:43:18,640 --> 00:43:23,400
+should try to D duplicate or sample
+
+1026
+00:43:20,920 --> 00:43:25,480
+without replacement because if you get
+
+1027
+00:43:23,400 --> 00:43:27,559
+multiple outputs here it messes up your
+
+1028
+00:43:25,480 --> 00:43:30,680
+equations if you basically uh have the
+
+1029
+00:43:27,559 --> 00:43:30,680
+same one in there multiple
+
+1030
+00:43:32,160 --> 00:43:37,800
+times cool so so this is a really simple
+
+1031
+00:43:35,880 --> 00:43:40,079
+example of how you can do minimal risk
+
+1032
+00:43:37,800 --> 00:43:42,119
+training but now I want to get into uh
+
+1033
+00:43:40,079 --> 00:43:44,640
+like reinforcement learning which is the
+
+1034
+00:43:42,119 --> 00:43:48,119
+framing that most um
+
+1035
+00:43:44,640 --> 00:43:50,760
+modern Works about this Paulo uh one
+
+1036
+00:43:48,119 --> 00:43:52,559
+thing I should mention is there are
+
+1037
+00:43:50,760 --> 00:43:55,240
+actually other alternatives to learning
+
+1038
+00:43:52,559 --> 00:43:57,359
+from uh human feedback including like
+
+1039
+00:43:55,240 --> 00:43:59,359
+margin loss margin based losses and
+
+1040
+00:43:57,359 --> 00:44:00,960
+other stuff like that but most people
+
+1041
+00:43:59,359 --> 00:44:03,440
+nowadays use reinforcement learning so
+
+1042
+00:44:00,960 --> 00:44:06,359
+I'm only going to cover that
+
+1043
+00:44:03,440 --> 00:44:08,440
+here so what is reinforcement learning
+
+1044
+00:44:06,359 --> 00:44:11,000
+um learning reinforcement learning is
+
+1045
+00:44:08,440 --> 00:44:14,559
+learning where we have an environment uh
+
+1046
+00:44:11,000 --> 00:44:16,079
+x uh ability to make actions a and get a
+
+1047
+00:44:14,559 --> 00:44:20,160
+delayed reward
+
+1048
+00:44:16,079 --> 00:44:21,880
+R and um there's a really nice example
+
+1049
+00:44:20,160 --> 00:44:24,400
+uh if you're not familiar with the
+
+1050
+00:44:21,880 --> 00:44:27,480
+basics of policy gradient by Andre
+
+1051
+00:44:24,400 --> 00:44:28,800
+karpathy which I linked in the um in the
+
+1052
+00:44:27,480 --> 00:44:29,680
+recommended reading so you can take a
+
+1053
+00:44:28,800 --> 00:44:34,680
+look at
+
+1054
+00:44:29,680 --> 00:44:37,240
+that um but in that example gives an
+
+1055
+00:44:34,680 --> 00:44:39,440
+example of pong uh where you're playing
+
+1056
+00:44:37,240 --> 00:44:42,640
+the game pong where X is your observed
+
+1057
+00:44:39,440 --> 00:44:45,640
+image a is up or down and R is the wind
+
+1058
+00:44:42,640 --> 00:44:47,480
+loss at the end of the game uh does
+
+1059
+00:44:45,640 --> 00:44:50,559
+anyone have an idea about uh what this
+
+1060
+00:44:47,480 --> 00:44:52,119
+looks like for any arbitrary NLP task
+
+1061
+00:44:50,559 --> 00:44:56,520
+that we might want to do reinforcement
+
+1062
+00:44:52,119 --> 00:44:59,040
+learning for so what what is X what is a
+
+1063
+00:44:56,520 --> 00:44:59,040
+and what is
+
+1064
+00:45:00,040 --> 00:45:04,680
+are pick your favorite uh your favorite
+
+1065
+00:45:06,920 --> 00:45:09,920
+Trask
+
+1066
+00:45:10,960 --> 00:45:18,400
+anybody
+
+1067
+00:45:12,520 --> 00:45:18,400
+yeah be or what what's X first
+
+1068
+00:45:19,680 --> 00:45:28,720
+yeah you have generate okay is the
+
+1069
+00:45:24,440 --> 00:45:29,720
+next be like the Buton like whether or
+
+1070
+00:45:28,720 --> 00:45:32,520
+not
+
+1071
+00:45:29,720 --> 00:45:35,240
+you okay yeah I I think this is very
+
+1072
+00:45:32,520 --> 00:45:37,119
+close just to repeat it it's like X is
+
+1073
+00:45:35,240 --> 00:45:39,599
+what you've generated so far a is the
+
+1074
+00:45:37,119 --> 00:45:41,559
+next token and R is the button that the
+
+1075
+00:45:39,599 --> 00:45:45,400
+user clicks about whether it's good or
+
+1076
+00:45:41,559 --> 00:45:46,920
+not um I think that's reasonably good
+
+1077
+00:45:45,400 --> 00:45:48,760
+although I don't know if we'd expect
+
+1078
+00:45:46,920 --> 00:45:52,960
+them to click the button every token we
+
+1079
+00:45:48,760 --> 00:45:54,880
+generate right so um it might be that X
+
+1080
+00:45:52,960 --> 00:45:57,880
+is the conversational history up till
+
+1081
+00:45:54,880 --> 00:46:02,319
+this point um a
+
+1082
+00:45:57,880 --> 00:46:04,280
+a could be a next token generation and
+
+1083
+00:46:02,319 --> 00:46:06,520
+then R is a reward we get in an
+
+1084
+00:46:04,280 --> 00:46:08,280
+arbitrary time point it might not be
+
+1085
+00:46:06,520 --> 00:46:09,960
+like immediately after generating the
+
+1086
+00:46:08,280 --> 00:46:12,040
+next token but it might be later and
+
+1087
+00:46:09,960 --> 00:46:13,480
+that's actually really really important
+
+1088
+00:46:12,040 --> 00:46:15,040
+from the point of view of reinforcement
+
+1089
+00:46:13,480 --> 00:46:19,599
+learning and I'll I'll talk about that
+
+1090
+00:46:15,040 --> 00:46:23,040
+in a second um anyone have an idea from
+
+1091
+00:46:19,599 --> 00:46:24,960
+I don't know uh code generation or
+
+1092
+00:46:23,040 --> 00:46:28,119
+translation or some other
+
+1093
+00:46:24,960 --> 00:46:31,160
+things C generation maybe s is a
+
+1094
+00:46:28,119 --> 00:46:33,040
+compiler or like the gra scpt and then
+
+1095
+00:46:31,160 --> 00:46:37,000
+the
+
+1096
+00:46:33,040 --> 00:46:42,520
+is the actual code that right and reward
+
+1097
+00:46:37,000 --> 00:46:44,839
+is yep um so X could be the compiler
+
+1098
+00:46:42,520 --> 00:46:47,559
+it's probably the compiler and all of
+
+1099
+00:46:44,839 --> 00:46:50,200
+the surrounding code context like what
+
+1100
+00:46:47,559 --> 00:46:52,520
+what is the natural language output and
+
+1101
+00:46:50,200 --> 00:46:53,960
+it's also um you know what is the
+
+1102
+00:46:52,520 --> 00:46:57,280
+project that you're you're working on
+
+1103
+00:46:53,960 --> 00:47:00,079
+and stuff like that um a i think
+
+1104
+00:46:57,280 --> 00:47:02,800
+typically we would treat each token in
+
+1105
+00:47:00,079 --> 00:47:04,160
+the code to be an action um and then R
+
+1106
+00:47:02,800 --> 00:47:06,599
+would be the reward after a long
+
+1107
+00:47:04,160 --> 00:47:08,640
+sequence of actions um and it could be
+
+1108
+00:47:06,599 --> 00:47:11,119
+the reward from the compiler it could be
+
+1109
+00:47:08,640 --> 00:47:13,160
+the reward from a code readability model
+
+1110
+00:47:11,119 --> 00:47:15,720
+it could be the reward from a speed
+
+1111
+00:47:13,160 --> 00:47:17,079
+execution speed and stuff like that so
+
+1112
+00:47:15,720 --> 00:47:18,839
+like one of the interesting things about
+
+1113
+00:47:17,079 --> 00:47:22,640
+R is you can be really creative about
+
+1114
+00:47:18,839 --> 00:47:25,400
+how you form R um which is not easy to
+
+1115
+00:47:22,640 --> 00:47:27,319
+do uh if you're just doing maximum
+
+1116
+00:47:25,400 --> 00:47:29,240
+likelihood also so you can come up with
+
+1117
+00:47:27,319 --> 00:47:32,920
+a r that really matches with like what
+
+1118
+00:47:29,240 --> 00:47:36,559
+you want um what you want in an output
+
+1119
+00:47:32,920 --> 00:47:40,079
+so why reinforcement learning in NLP um
+
+1120
+00:47:36,559 --> 00:47:42,599
+and I think there's basically three um
+
+1121
+00:47:40,079 --> 00:47:44,240
+three answers the first one is you have
+
+1122
+00:47:42,599 --> 00:47:49,000
+a typical reinforcement learning
+
+1123
+00:47:44,240 --> 00:47:51,119
+scenario um where you have a dialogue
+
+1124
+00:47:49,000 --> 00:47:52,720
+where you get lots of responses and then
+
+1125
+00:47:51,119 --> 00:47:54,559
+you get a reward at the end so the
+
+1126
+00:47:52,720 --> 00:47:57,359
+thumbs up and thumbs down from humans is
+
+1127
+00:47:54,559 --> 00:47:59,839
+a very typical example of
+
+1128
+00:47:57,359 --> 00:48:02,800
+uh reinforcement learning because you
+
+1129
+00:47:59,839 --> 00:48:05,000
+get a delayed reward uh at some point in
+
+1130
+00:48:02,800 --> 00:48:07,599
+the dialogue when a human presses up or
+
+1131
+00:48:05,000 --> 00:48:09,280
+down um another like actually more
+
+1132
+00:48:07,599 --> 00:48:11,680
+technical scenario where reinforcement
+
+1133
+00:48:09,280 --> 00:48:14,960
+learning has been used um for a long
+
+1134
+00:48:11,680 --> 00:48:17,400
+time is call centers so we've had
+
+1135
+00:48:14,960 --> 00:48:20,680
+dialogue systems for call centers and
+
+1136
+00:48:17,400 --> 00:48:23,160
+then if you complete a ticket purchase
+
+1137
+00:48:20,680 --> 00:48:24,839
+um or you complete resolve a ticket
+
+1138
+00:48:23,160 --> 00:48:27,480
+without ever having to go to a human
+
+1139
+00:48:24,839 --> 00:48:30,800
+operator you get a really big reward
+
+1140
+00:48:27,480 --> 00:48:33,640
+if you have to go to the human operator
+
+1141
+00:48:30,800 --> 00:48:36,400
+you get maybe a smaller reward and if
+
+1142
+00:48:33,640 --> 00:48:39,200
+the person yells at you and hangs up
+
+1143
+00:48:36,400 --> 00:48:41,640
+then you get a really negative reward so
+
+1144
+00:48:39,200 --> 00:48:43,040
+um this is kind of the typical example
+
+1145
+00:48:41,640 --> 00:48:45,599
+reinforcement learning has been used for
+
+1146
+00:48:43,040 --> 00:48:48,520
+a long time there another example is if
+
+1147
+00:48:45,599 --> 00:48:53,280
+you have like latent variables uh chains
+
+1148
+00:48:48,520 --> 00:48:55,799
+of thought where um you decide the
+
+1149
+00:48:53,280 --> 00:48:58,839
+latent variable and then get a reward um
+
+1150
+00:48:55,799 --> 00:49:02,799
+you get a reward based Bas on how those
+
+1151
+00:48:58,839 --> 00:49:03,920
+latent variables affect the output so um
+
+1152
+00:49:02,799 --> 00:49:07,200
+this
+
+1153
+00:49:03,920 --> 00:49:09,799
+is uh this is another example
+
+1154
+00:49:07,200 --> 00:49:12,599
+because the Chain of Thought itself
+
+1155
+00:49:09,799 --> 00:49:13,880
+might not actually be good you might
+
+1156
+00:49:12,599 --> 00:49:15,839
+have a bad Chain of Thought and still
+
+1157
+00:49:13,880 --> 00:49:17,760
+get the correct answer so you don't
+
+1158
+00:49:15,839 --> 00:49:19,640
+actually know for sure that a chain of
+
+1159
+00:49:17,760 --> 00:49:22,359
+thought that was automatically generated
+
+1160
+00:49:19,640 --> 00:49:24,799
+is good or not but um that so that kind
+
+1161
+00:49:22,359 --> 00:49:27,000
+of makes it a reinforcement learning
+
+1162
+00:49:24,799 --> 00:49:29,520
+problem and another thing is you might
+
+1163
+00:49:27,000 --> 00:49:32,520
+have a sequence level evaluation metric
+
+1164
+00:49:29,520 --> 00:49:34,240
+um so that you can't optimize the
+
+1165
+00:49:32,520 --> 00:49:36,839
+evaluation metric without uh first
+
+1166
+00:49:34,240 --> 00:49:38,480
+generating the whole like sequence so
+
+1167
+00:49:36,839 --> 00:49:40,880
+that would be any of the evaluation
+
+1168
+00:49:38,480 --> 00:49:42,400
+metrics that I talked about before so um
+
+1169
+00:49:40,880 --> 00:49:44,720
+these are three scenarios where you can
+
+1170
+00:49:42,400 --> 00:49:47,079
+use reinforcement
+
+1171
+00:49:44,720 --> 00:49:50,000
+planning so
+
+1172
+00:49:47,079 --> 00:49:51,400
+um I'm going to make a few steps through
+
+1173
+00:49:50,000 --> 00:49:54,640
+but like let's start again with our
+
+1174
+00:49:51,400 --> 00:49:57,359
+supervised mle loss and uh that's just
+
+1175
+00:49:54,640 --> 00:50:01,799
+the log probability here um in the
+
+1176
+00:49:57,359 --> 00:50:04,160
+context of reinforcement learning this
+
+1177
+00:50:01,799 --> 00:50:07,079
+is also called imitation
+
+1178
+00:50:04,160 --> 00:50:08,880
+learning because um essentially you're
+
+1179
+00:50:07,079 --> 00:50:12,680
+learning how to perform actions by
+
+1180
+00:50:08,880 --> 00:50:14,559
+imitating a teacher um and imitation
+
+1181
+00:50:12,680 --> 00:50:15,960
+learning is not just supervised mle
+
+1182
+00:50:14,559 --> 00:50:18,440
+there's also other varieties of
+
+1183
+00:50:15,960 --> 00:50:21,440
+imitation learning but um this is one
+
+1184
+00:50:18,440 --> 00:50:21,440
+variety of imitation
+
+1185
+00:50:22,520 --> 00:50:27,640
+learning the next thing I'd like to talk
+
+1186
+00:50:24,599 --> 00:50:30,079
+about is self-training and basically
+
+1187
+00:50:27,640 --> 00:50:31,760
+self-training the idea is that you
+
+1188
+00:50:30,079 --> 00:50:33,720
+sample or argmax according to the
+
+1189
+00:50:31,760 --> 00:50:36,119
+current model so you have your current
+
+1190
+00:50:33,720 --> 00:50:38,000
+model and you get a sample from it and
+
+1191
+00:50:36,119 --> 00:50:41,520
+then you use the sample or samples to
+
+1192
+00:50:38,000 --> 00:50:43,680
+maximize likelihood so um basically
+
+1193
+00:50:41,520 --> 00:50:47,520
+instead of doing maximum likelihood with
+
+1194
+00:50:43,680 --> 00:50:49,520
+respect to the a gold standard output
+
+1195
+00:50:47,520 --> 00:50:51,280
+you're doing it with respect to your own
+
+1196
+00:50:49,520 --> 00:50:55,280
+output
+
+1197
+00:50:51,280 --> 00:50:55,280
+so does this seem like a good
+
+1198
+00:50:55,640 --> 00:51:03,880
+idea I see a few people shaking heads um
+
+1199
+00:51:00,480 --> 00:51:03,880
+any ideas why this is not a good
+
+1200
+00:51:04,680 --> 00:51:07,680
+idea
+
+1201
+00:51:15,040 --> 00:51:20,599
+yeah yeah exactly so if you don't have
+
+1202
+00:51:17,720 --> 00:51:23,760
+any access to any notion well it's good
+
+1203
+00:51:20,599 --> 00:51:27,480
+um this will be optimizing towards good
+
+1204
+00:51:23,760 --> 00:51:28,839
+outputs and bad outputs right so um your
+
+1205
+00:51:27,480 --> 00:51:30,200
+model might be outputting bad outputs
+
+1206
+00:51:28,839 --> 00:51:32,839
+and you're just reinforcing the errors
+
+1207
+00:51:30,200 --> 00:51:35,160
+set the model R already nonetheless like
+
+1208
+00:51:32,839 --> 00:51:37,799
+self trining actually improves your
+
+1209
+00:51:35,160 --> 00:51:39,680
+accuracy somewhat in some cases like for
+
+1210
+00:51:37,799 --> 00:51:43,040
+example if your accuracy is if your
+
+1211
+00:51:39,680 --> 00:51:45,520
+model is Right more often than not um
+
+1212
+00:51:43,040 --> 00:51:49,119
+basically optimizing towards the more
+
+1213
+00:51:45,520 --> 00:51:51,720
+often the not right outputs can actually
+
+1214
+00:51:49,119 --> 00:51:53,640
+um due to the implicit regularization
+
+1215
+00:51:51,720 --> 00:51:55,000
+that models have and early stopping and
+
+1216
+00:51:53,640 --> 00:51:56,559
+other things like that it can actually
+
+1217
+00:51:55,000 --> 00:51:59,280
+move you in the right direction and
+
+1218
+00:51:56,559 --> 00:52:01,559
+improve accuracy
+
+1219
+00:51:59,280 --> 00:52:05,000
+um
+
+1220
+00:52:01,559 --> 00:52:06,640
+so there are alternatives to this that
+
+1221
+00:52:05,000 --> 00:52:09,520
+further improve accuracy so like for
+
+1222
+00:52:06,640 --> 00:52:12,720
+example if you have multiple models and
+
+1223
+00:52:09,520 --> 00:52:16,200
+um you only generate sentences where the
+
+1224
+00:52:12,720 --> 00:52:17,760
+models agree then this can improve your
+
+1225
+00:52:16,200 --> 00:52:20,000
+uh overall accuracy
+
+1226
+00:52:17,760 --> 00:52:24,240
+further um this is called code training
+
+1227
+00:52:20,000 --> 00:52:27,799
+it was actually uh created by uh uh
+
+1228
+00:52:24,240 --> 00:52:30,160
+people at at CMU as well and another
+
+1229
+00:52:27,799 --> 00:52:32,280
+successful alternative uh is adding
+
+1230
+00:52:30,160 --> 00:52:34,920
+noise to the input to match the noise
+
+1231
+00:52:32,280 --> 00:52:38,760
+that you find in the output so if you uh
+
+1232
+00:52:34,920 --> 00:52:40,720
+add like word uh word-based Dropout or
+
+1233
+00:52:38,760 --> 00:52:44,000
+other things like that this can also
+
+1234
+00:52:40,720 --> 00:52:47,400
+help uh accommodate these things but
+
+1235
+00:52:44,000 --> 00:52:48,920
+anyway um so self trining is is useful
+
+1236
+00:52:47,400 --> 00:52:50,480
+but there are better Alternatives if you
+
+1237
+00:52:48,920 --> 00:52:54,079
+can get a reward
+
+1238
+00:52:50,480 --> 00:52:55,559
+function so um the simplest variety of
+
+1239
+00:52:54,079 --> 00:52:56,960
+this is something called policy gradient
+
+1240
+00:52:55,559 --> 00:52:59,720
+or reinforce
+
+1241
+00:52:56,960 --> 00:53:02,319
+um or more specifically reinforce and
+
+1242
+00:52:59,720 --> 00:53:06,280
+basically what this does is this adds a
+
+1243
+00:53:02,319 --> 00:53:08,359
+term that scales the loss by the reward
+
+1244
+00:53:06,280 --> 00:53:12,400
+so if you can get a reward for each
+
+1245
+00:53:08,359 --> 00:53:15,680
+output basically this
+
+1246
+00:53:12,400 --> 00:53:18,119
+um you uh instead of doing self trining
+
+1247
+00:53:15,680 --> 00:53:21,760
+entirely by itself you multiply it by a
+
+1248
+00:53:18,119 --> 00:53:23,119
+reward and this allows you to increase
+
+1249
+00:53:21,760 --> 00:53:24,640
+the likelihood of things that get a high
+
+1250
+00:53:23,119 --> 00:53:28,440
+reward decrease the likelihood of things
+
+1251
+00:53:24,640 --> 00:53:28,440
+that get a low reward
+
+1252
+00:53:29,680 --> 00:53:34,960
+so uh a brief quiz here under what
+
+1253
+00:53:32,440 --> 00:53:37,599
+conditions is this equal equivalent to
+
+1254
+00:53:34,960 --> 00:53:41,480
+ml or essentially equivalent to maximum
+
+1255
+00:53:37,599 --> 00:53:43,079
+leg uh estimation and so like in order
+
+1256
+00:53:41,480 --> 00:53:45,480
+to make this quiz easier I'll go back to
+
+1257
+00:53:43,079 --> 00:53:47,720
+maximum likelihood estimation so it
+
+1258
+00:53:45,480 --> 00:53:50,359
+looked a bit like this um you calculated
+
+1259
+00:53:47,720 --> 00:53:53,440
+the log probability of the true output
+
+1260
+00:53:50,359 --> 00:53:55,440
+and now let me go uh to
+
+1261
+00:53:53,440 --> 00:53:56,960
+here any
+
+1262
+00:53:55,440 --> 00:54:00,119
+ideas
+
+1263
+00:53:56,960 --> 00:54:05,040
+yeah when your reward equals to
+
+1264
+00:54:00,119 --> 00:54:05,040
+one some sometimes in zero other times
+
+1265
+00:54:07,760 --> 00:54:10,960
+what any
+
+1266
+00:54:12,760 --> 00:54:17,520
+ideas what when when does your reward
+
+1267
+00:54:15,280 --> 00:54:19,640
+need to be equal to one in order to make
+
+1268
+00:54:17,520 --> 00:54:23,400
+this
+
+1269
+00:54:19,640 --> 00:54:23,400
+equation equivalent this
+
+1270
+00:54:24,960 --> 00:54:31,680
+equation yeah when Y and Y hat are the
+
+1271
+00:54:27,319 --> 00:54:36,119
+same so um basically
+
+1272
+00:54:31,680 --> 00:54:38,880
+this objective is equivalent to the mle
+
+1273
+00:54:36,119 --> 00:54:43,160
+objective when you're using a zero1
+
+1274
+00:54:38,880 --> 00:54:44,480
+loss um where or you're using an
+
+1275
+00:54:43,160 --> 00:54:46,359
+evaluation function that gives you a
+
+1276
+00:54:44,480 --> 00:54:50,920
+score of one when it's exact match and
+
+1277
+00:54:46,359 --> 00:54:51,720
+zero when it's not exact match so um but
+
+1278
+00:54:50,920 --> 00:54:54,480
+that
+
+1279
+00:54:51,720 --> 00:54:56,440
+also demonstrates that this can be more
+
+1280
+00:54:54,480 --> 00:54:58,400
+flexible because you can have other
+
+1281
+00:54:56,440 --> 00:55:00,160
+rewards that are not just one and zero
+
+1282
+00:54:58,400 --> 00:55:02,599
+for exact match but you can use things
+
+1283
+00:55:00,160 --> 00:55:05,359
+that give you partial credit you can use
+
+1284
+00:55:02,599 --> 00:55:06,880
+things that uplate multiple potential uh
+
+1285
+00:55:05,359 --> 00:55:08,880
+potentially correct outputs and other
+
+1286
+00:55:06,880 --> 00:55:13,400
+things like
+
+1287
+00:55:08,880 --> 00:55:17,160
+that so one problem with these methods
+
+1288
+00:55:13,400 --> 00:55:21,799
+is um how do we know which action led to
+
+1289
+00:55:17,160 --> 00:55:24,720
+the reward so the best scenario is after
+
+1290
+00:55:21,799 --> 00:55:26,359
+each action you get a reward so after
+
+1291
+00:55:24,720 --> 00:55:28,960
+each token that you generated you get
+
+1292
+00:55:26,359 --> 00:55:31,240
+get a thumbs up or thumbs down uh from
+
+1293
+00:55:28,960 --> 00:55:34,280
+the user about whether they like that
+
+1294
+00:55:31,240 --> 00:55:36,000
+token or not um and how much happier
+
+1295
+00:55:34,280 --> 00:55:37,720
+they are after you generated that token
+
+1296
+00:55:36,000 --> 00:55:42,400
+than they were before you generated that
+
+1297
+00:55:37,720 --> 00:55:44,200
+token um the problem with this is that
+
+1298
+00:55:42,400 --> 00:55:45,799
+that's completely infeasible right like
+
+1299
+00:55:44,200 --> 00:55:47,039
+every time after you use chat GPD you're
+
+1300
+00:55:45,799 --> 00:55:50,480
+not going to press thumbs up and thumbs
+
+1301
+00:55:47,039 --> 00:55:52,559
+down after each token so um in reality
+
+1302
+00:55:50,480 --> 00:55:55,559
+what we get is usually we get it at the
+
+1303
+00:55:52,559 --> 00:55:57,000
+end of uh roll out of many many
+
+1304
+00:55:55,559 --> 00:55:58,640
+different actions and we're not sure
+
+1305
+00:55:57,000 --> 00:55:59,720
+which action is responsible for giving
+
+1306
+00:55:58,640 --> 00:56:02,559
+us the
+
+1307
+00:55:59,720 --> 00:56:05,440
+reward and
+
+1308
+00:56:02,559 --> 00:56:08,000
+so there's a few typical ways of dealing
+
+1309
+00:56:05,440 --> 00:56:09,640
+with this um the most typical way of
+
+1310
+00:56:08,000 --> 00:56:13,359
+dealing with this right now is just not
+
+1311
+00:56:09,640 --> 00:56:15,440
+dealing with it um and just hoping that
+
+1312
+00:56:13,359 --> 00:56:17,200
+your optimization algorithm internally
+
+1313
+00:56:15,440 --> 00:56:21,480
+will be able to do credit
+
+1314
+00:56:17,200 --> 00:56:24,520
+assignment um and so what that entails
+
+1315
+00:56:21,480 --> 00:56:27,319
+is essentially you um give an equal
+
+1316
+00:56:24,520 --> 00:56:29,880
+reward for each token in the output
+
+1317
+00:56:27,319 --> 00:56:32,480
+other ways that you can deal with it are
+
+1318
+00:56:29,880 --> 00:56:35,640
+um you can assign decaying rewards from
+
+1319
+00:56:32,480 --> 00:56:37,559
+future events so like let's say let's
+
+1320
+00:56:35,640 --> 00:56:41,839
+say you're talking about a chat bot for
+
+1321
+00:56:37,559 --> 00:56:44,119
+example maybe this is the the most uh
+
+1322
+00:56:41,839 --> 00:56:46,599
+kind of intuitive way of thinking about
+
+1323
+00:56:44,119 --> 00:56:50,400
+it but you you have a chat bot you have
+
+1324
+00:56:46,599 --> 00:56:52,599
+like 20 chat turns and you have the user
+
+1325
+00:56:50,400 --> 00:56:55,640
+give a thumbs up or a thumbs down on the
+
+1326
+00:56:52,599 --> 00:56:58,920
+20th chat turn there you would assign a
+
+1327
+00:56:55,640 --> 00:57:01,440
+reward of um like let's say it gave a
+
+1328
+00:56:58,920 --> 00:57:03,640
+thumbs up there you would re assign a
+
+1329
+00:57:01,440 --> 00:57:06,559
+reward of one for the previous chat turn
+
+1330
+00:57:03,640 --> 00:57:09,839
+a reward of like 0.5 for the second to
+
+1331
+00:57:06,559 --> 00:57:11,720
+previous chat term a reward of 0.25 for
+
+1332
+00:57:09,839 --> 00:57:14,319
+the third to previous chat term to
+
+1333
+00:57:11,720 --> 00:57:16,160
+basically say yeah like the user is
+
+1334
+00:57:14,319 --> 00:57:18,240
+feeling good at the moment they gave the
+
+1335
+00:57:16,160 --> 00:57:20,359
+thumbs up and that's probably more
+
+1336
+00:57:18,240 --> 00:57:23,400
+likely due to the things that happened
+
+1337
+00:57:20,359 --> 00:57:23,400
+recently so
+
+1338
+00:57:23,559 --> 00:57:28,119
+yeah we have a
+
+1339
+00:57:26,680 --> 00:57:32,280
+like not
+
+1340
+00:57:28,119 --> 00:57:34,160
+learning so the reward model can be any
+
+1341
+00:57:32,280 --> 00:57:35,839
+of the methods that I talked about
+
+1342
+00:57:34,160 --> 00:57:37,480
+before so it can be human feedback
+
+1343
+00:57:35,839 --> 00:57:39,000
+directly like a thumbs up or a thumbs
+
+1344
+00:57:37,480 --> 00:57:42,200
+down it could also be from a reward
+
+1345
+00:57:39,000 --> 00:57:44,599
+model uh that was pre-trained you could
+
+1346
+00:57:42,200 --> 00:57:47,680
+also theoretically learn the reward
+
+1347
+00:57:44,599 --> 00:57:52,720
+model simultaneously but you'd have to
+
+1348
+00:57:47,680 --> 00:57:55,200
+simultaneously with the model itself um
+
+1349
+00:57:52,720 --> 00:57:57,280
+so yeah I'm going to talk a little bit
+
+1350
+00:57:55,200 --> 00:58:00,359
+about DP which kind of does that a
+
+1351
+00:57:57,280 --> 00:58:01,720
+little bit but um I I would basically
+
+1352
+00:58:00,359 --> 00:58:03,160
+say that wherever you're getting your
+
+1353
+00:58:01,720 --> 00:58:06,280
+reward is probably from one of the
+
+1354
+00:58:03,160 --> 00:58:06,280
+things I talked about earlier
+
+1355
+00:58:06,359 --> 00:58:14,960
+today cool any other
+
+1356
+00:58:09,319 --> 00:58:17,720
+questions okay um so that's the basic
+
+1357
+00:58:14,960 --> 00:58:20,640
+the basic idea the very simplest thing
+
+1358
+00:58:17,720 --> 00:58:23,359
+that you can do is you can just sample
+
+1359
+00:58:20,640 --> 00:58:26,079
+um optimize the subjective function this
+
+1360
+00:58:23,359 --> 00:58:28,359
+is dead easy you it's not hard to imp
+
+1361
+00:58:26,079 --> 00:58:30,799
+imp it all as long as you have some
+
+1362
+00:58:28,359 --> 00:58:32,760
+source of reward signal um but the
+
+1363
+00:58:30,799 --> 00:58:35,559
+problem is uh reinforcement learning can
+
+1364
+00:58:32,760 --> 00:58:38,599
+be very unstable and it's hard to get it
+
+1365
+00:58:35,559 --> 00:58:40,160
+to uh you know work properly if you uh
+
+1366
+00:58:38,599 --> 00:58:42,400
+don't do some additional tricks so I'd
+
+1367
+00:58:40,160 --> 00:58:45,720
+like to talk about this
+
+1368
+00:58:42,400 --> 00:58:45,720
+next oh yeah
+
+1369
+00:58:48,880 --> 00:58:51,880
+sir
+
+1370
+00:58:55,039 --> 00:58:58,039
+yeah
+
+1371
+00:59:03,280 --> 00:59:08,960
+yeah the typical the typical way is you
+
+1372
+00:59:05,440 --> 00:59:12,960
+just have an exponential decay um so you
+
+1373
+00:59:08,960 --> 00:59:16,200
+you multiply each time by what 0.5 0. or
+
+1374
+00:59:12,960 --> 00:59:19,400
+something like that
+
+1375
+00:59:16,200 --> 00:59:19,400
+um from
+
+1376
+00:59:20,319 --> 00:59:27,720
+A6 um cool okay
+
+1377
+00:59:25,039 --> 00:59:30,720
+so
+
+1378
+00:59:27,720 --> 00:59:33,319
+and that's one option and sorry just to
+
+1379
+00:59:30,720 --> 00:59:35,760
+clarify the most common option nowadays
+
+1380
+00:59:33,319 --> 00:59:37,920
+um at least from the point of view of
+
+1381
+00:59:35,760 --> 00:59:39,839
+models is not to Decay it at all and
+
+1382
+00:59:37,920 --> 00:59:43,880
+just assign the same amount for each
+
+1383
+00:59:39,839 --> 00:59:45,319
+token um I'm not actually 100% sure what
+
+1384
+00:59:43,880 --> 00:59:47,319
+people are doing with respect to like
+
+1385
+00:59:45,319 --> 00:59:49,280
+long chat things I think probably
+
+1386
+00:59:47,319 --> 00:59:51,720
+they're only assigning it to the current
+
+1387
+00:59:49,280 --> 00:59:54,240
+like utterance and then not optimizing
+
+1388
+00:59:51,720 --> 00:59:57,240
+the previous utterances so like if they
+
+1389
+00:59:54,240 --> 00:59:59,039
+get a thumbs up or thumbs down signal um
+
+1390
+00:59:57,240 --> 01:00:00,720
+then they they would assign an
+
+1391
+00:59:59,039 --> 01:00:02,440
+equivalent reward for all of the tokens
+
+1392
+01:00:00,720 --> 01:00:04,640
+and the current utterance and zero
+
+1393
+01:00:02,440 --> 01:00:06,119
+reward for the previous ones but I'm not
+
+1394
+01:00:04,640 --> 01:00:08,480
+100% sure about that there might be
+
+1395
+01:00:06,119 --> 01:00:11,200
+other methods that people are
+
+1396
+01:00:08,480 --> 01:00:13,960
+using um
+
+1397
+01:00:11,200 --> 01:00:16,680
+cool so uh stabilizing reinforcement
+
+1398
+01:00:13,960 --> 01:00:18,520
+learning so um stabilizing reinforcement
+
+1399
+01:00:16,680 --> 01:00:21,839
+learning there's a lot of reasons why
+
+1400
+01:00:18,520 --> 01:00:23,880
+it's unstable um the first reason is
+
+1401
+01:00:21,839 --> 01:00:27,200
+you're sampling an individual output and
+
+1402
+01:00:23,880 --> 01:00:30,160
+calculating the um uh calculating based
+
+1403
+01:00:27,200 --> 01:00:32,039
+on the S individual sampled output and
+
+1404
+01:00:30,160 --> 01:00:33,440
+then there's an Infinity of other
+
+1405
+01:00:32,039 --> 01:00:36,480
+outputs that you could be optimizing
+
+1406
+01:00:33,440 --> 01:00:39,119
+over for mle this is not a problem
+
+1407
+01:00:36,480 --> 01:00:41,319
+because for mle you're always
+
+1408
+01:00:39,119 --> 01:00:45,359
+contrasting the gold standard output to
+
+1409
+01:00:41,319 --> 01:00:46,599
+all of the other outputs in the space um
+
+1410
+01:00:45,359 --> 01:00:48,280
+and you're saying I want to upweight the
+
+1411
+01:00:46,599 --> 01:00:51,200
+gold standard output and down we all of
+
+1412
+01:00:48,280 --> 01:00:53,039
+the other ones but for reinforcement
+
+1413
+01:00:51,200 --> 01:00:54,760
+learning you only have a single sampled
+
+1414
+01:00:53,039 --> 01:00:57,520
+output that output might be wrong and
+
+1415
+01:00:54,760 --> 01:00:59,359
+that's a source of inst ility this is
+
+1416
+01:00:57,520 --> 01:01:02,079
+particularly a problem when using bigger
+
+1417
+01:00:59,359 --> 01:01:05,960
+output spaces like all of the in the
+
+1418
+01:01:02,079 --> 01:01:07,920
+vocabul another problem is uh anytime
+
+1419
+01:01:05,960 --> 01:01:11,599
+you start using negative
+
+1420
+01:01:07,920 --> 01:01:15,160
+rewards um because if you start using
+
+1421
+01:01:11,599 --> 01:01:17,559
+negative rewards those rewards will be
+
+1422
+01:01:15,160 --> 01:01:19,520
+downweighting the probability of a
+
+1423
+01:01:17,559 --> 01:01:20,680
+particular output sequence and that
+
+1424
+01:01:19,520 --> 01:01:22,440
+might be a good idea maybe you're
+
+1425
+01:01:20,680 --> 01:01:24,319
+getting a toxic output or something like
+
+1426
+01:01:22,440 --> 01:01:25,960
+that and you want to down it but at the
+
+1427
+01:01:24,319 --> 01:01:28,280
+same time in addition to that toxic
+
+1428
+01:01:25,960 --> 01:01:30,000
+output there's like you know a
+
+1429
+01:01:28,280 --> 01:01:31,599
+combinatorial number of completely
+
+1430
+01:01:30,000 --> 01:01:33,880
+nonsense outputs that aren't even
+
+1431
+01:01:31,599 --> 01:01:36,599
+English and so basically you can start
+
+1432
+01:01:33,880 --> 01:01:38,920
+diverge from the N starting start to
+
+1433
+01:01:36,599 --> 01:01:40,799
+diverge from the natural like language
+
+1434
+01:01:38,920 --> 01:01:44,720
+modeling distribution that you have
+
+1435
+01:01:40,799 --> 01:01:49,079
+before so this is a big uh a big
+
+1436
+01:01:44,720 --> 01:01:51,880
+problem so a number of uh strategies can
+
+1437
+01:01:49,079 --> 01:01:53,880
+be used to stabilize the first one is
+
+1438
+01:01:51,880 --> 01:01:55,480
+this is completely obvious right now and
+
+1439
+01:01:53,880 --> 01:01:57,240
+nobody in their right mind would avoid
+
+1440
+01:01:55,480 --> 01:02:00,119
+doing this but the first one is
+
+1441
+01:01:57,240 --> 01:02:02,839
+pre-training with mle and so you start
+
+1442
+01:02:00,119 --> 01:02:04,920
+with a pre-trained model um and then
+
+1443
+01:02:02,839 --> 01:02:09,359
+switch over to RL after you finished
+
+1444
+01:02:04,920 --> 01:02:11,520
+pre-training the model um and so
+
+1445
+01:02:09,359 --> 01:02:13,279
+this makes a lot of sense if you're
+
+1446
+01:02:11,520 --> 01:02:14,960
+training a language model which I assume
+
+1447
+01:02:13,279 --> 01:02:17,039
+that almost everybody in this class is
+
+1448
+01:02:14,960 --> 01:02:20,279
+going to be doing but it does only work
+
+1449
+01:02:17,039 --> 01:02:22,720
+in scenarios where you can run mle and
+
+1450
+01:02:20,279 --> 01:02:24,359
+so it doesn't work if you're predicting
+
+1451
+01:02:22,720 --> 01:02:27,240
+like latent variables that aren't
+
+1452
+01:02:24,359 --> 01:02:28,760
+included in the original space
+
+1453
+01:02:27,240 --> 01:02:31,960
+um it
+
+1454
+01:02:28,760 --> 01:02:34,279
+also doesn't work in a setting where
+
+1455
+01:02:31,960 --> 01:02:36,640
+like you want to learn a
+
+1456
+01:02:34,279 --> 01:02:40,799
+chatbot you want to learn a chatbot for
+
+1457
+01:02:36,640 --> 01:02:44,200
+customer service for a
+
+1458
+01:02:40,799 --> 01:02:48,039
+company that
+
+1459
+01:02:44,200 --> 01:02:49,960
+has like for example a product catalog
+
+1460
+01:02:48,039 --> 01:02:53,559
+that the language model has never seen
+
+1461
+01:02:49,960 --> 01:02:56,000
+before and so if the language model has
+
+1462
+01:02:53,559 --> 01:02:57,359
+no information about the product catalog
+
+1463
+01:02:56,000 --> 01:02:59,920
+whatsoever you don't provide it through
+
+1464
+01:02:57,359 --> 01:03:02,440
+rag or something like that it's going to
+
+1465
+01:02:59,920 --> 01:03:04,039
+have to explore infinitely or not
+
+1466
+01:03:02,440 --> 01:03:05,599
+infinitely but it's going to have to
+
+1467
+01:03:04,039 --> 01:03:08,359
+explore too large of a space and you're
+
+1468
+01:03:05,599 --> 01:03:10,000
+never going to converge with um with
+
+1469
+01:03:08,359 --> 01:03:12,359
+your language modeling objectives so you
+
+1470
+01:03:10,000 --> 01:03:15,000
+need to basically be able to create at
+
+1471
+01:03:12,359 --> 01:03:16,079
+least some supervised training data to
+
+1472
+01:03:15,000 --> 01:03:19,279
+train with
+
+1473
+01:03:16,079 --> 01:03:20,720
+mle um but assuming you can do that I'm
+
+1474
+01:03:19,279 --> 01:03:22,920
+assuming that almost everybody is going
+
+1475
+01:03:20,720 --> 01:03:26,400
+to do some sort of pre-training with
+
+1476
+01:03:22,920 --> 01:03:27,880
+ML um The Next Step that people use uh
+
+1477
+01:03:26,400 --> 01:03:30,520
+in reinforcement learning that's really
+
+1478
+01:03:27,880 --> 01:03:34,319
+important to stabilize is regularization
+
+1479
+01:03:30,520 --> 01:03:35,880
+to an existing model and you have an
+
+1480
+01:03:34,319 --> 01:03:39,039
+existing model and you want to prevent
+
+1481
+01:03:35,880 --> 01:03:40,559
+it from getting too far away and the
+
+1482
+01:03:39,039 --> 01:03:42,279
+reason why you want to do this is like
+
+1483
+01:03:40,559 --> 01:03:45,720
+let's say you start assigning a negative
+
+1484
+01:03:42,279 --> 01:03:47,440
+reward to toxic utterances for example
+
+1485
+01:03:45,720 --> 01:03:49,200
+if your model stops being a language
+
+1486
+01:03:47,440 --> 01:03:51,920
+model whatsoever that's a bad idea so
+
+1487
+01:03:49,200 --> 01:03:53,400
+you want to keep it as a language model
+
+1488
+01:03:51,920 --> 01:03:55,599
+keep it close enough to still being a
+
+1489
+01:03:53,400 --> 01:03:57,559
+competent language model while you know
+
+1490
+01:03:55,599 --> 01:03:59,599
+like removing the toxic
+
+1491
+01:03:57,559 --> 01:04:03,039
+utterances so there's a number of
+
+1492
+01:03:59,599 --> 01:04:05,680
+methods that people use to do this um uh
+
+1493
+01:04:03,039 --> 01:04:08,359
+the most prominent ones are kale
+
+1494
+01:04:05,680 --> 01:04:10,279
+regularization uh well so the the first
+
+1495
+01:04:08,359 --> 01:04:13,119
+most prominent one is K regularization
+
+1496
+01:04:10,279 --> 01:04:15,839
+and the way this works is basically in
+
+1497
+01:04:13,119 --> 01:04:19,400
+addition you add you have two
+
+1498
+01:04:15,839 --> 01:04:22,279
+terms the first term is a term that
+
+1499
+01:04:19,400 --> 01:04:25,760
+improves your reward so you have your
+
+1500
+01:04:22,279 --> 01:04:28,039
+old model where your old model is
+
+1501
+01:04:25,760 --> 01:04:31,279
+creating a
+
+1502
+01:04:28,039 --> 01:04:32,440
+probability uh it has a probability here
+
+1503
+01:04:31,279 --> 01:04:34,960
+and then you have the probability
+
+1504
+01:04:32,440 --> 01:04:38,160
+assigned by your new model and then you
+
+1505
+01:04:34,960 --> 01:04:41,200
+have your reward signal here and so this
+
+1506
+01:04:38,160 --> 01:04:43,599
+is basically improving the log odds or
+
+1507
+01:04:41,200 --> 01:04:46,960
+improving the odds of getting a good
+
+1508
+01:04:43,599 --> 01:04:49,720
+reward for high reward
+
+1509
+01:04:46,960 --> 01:04:52,920
+sequences separately from this you have
+
+1510
+01:04:49,720 --> 01:04:55,920
+this K regularization term and this K
+
+1511
+01:04:52,920 --> 01:04:58,119
+regularization term is keeping the
+
+1512
+01:04:55,920 --> 01:05:00,279
+scores of or it's keeping the
+
+1513
+01:04:58,119 --> 01:05:02,400
+probability distribution of your new
+
+1514
+01:05:00,279 --> 01:05:03,960
+model similar to the probability
+
+1515
+01:05:02,400 --> 01:05:09,200
+distribution of your old
+
+1516
+01:05:03,960 --> 01:05:11,359
+model and this beta parameter basically
+
+1517
+01:05:09,200 --> 01:05:15,240
+you can increase it or decrease it based
+
+1518
+01:05:11,359 --> 01:05:18,400
+on how similar you want to keep the um
+
+1519
+01:05:15,240 --> 01:05:18,400
+how similar you want to keep the
+
+1520
+01:05:20,720 --> 01:05:24,640
+model another method that people use is
+
+1521
+01:05:23,160 --> 01:05:29,279
+something called proximal policy
+
+1522
+01:05:24,640 --> 01:05:30,920
+optimization or or Po and this is a
+
+1523
+01:05:29,279 --> 01:05:33,920
+method that is based on
+
+1524
+01:05:30,920 --> 01:05:38,160
+clipping uh the
+
+1525
+01:05:33,920 --> 01:05:40,920
+outputs and we Define uh this ratio
+
+1526
+01:05:38,160 --> 01:05:43,880
+here so this ratio is equivalent to this
+
+1527
+01:05:40,920 --> 01:05:46,160
+here so it's basically um kind of the
+
+1528
+01:05:43,880 --> 01:05:47,839
+amount that you're learning or the
+
+1529
+01:05:46,160 --> 01:05:51,720
+amount that the new model up weights
+
+1530
+01:05:47,839 --> 01:05:54,039
+High reward sequences and so here we
+
+1531
+01:05:51,720 --> 01:05:58,200
+have the same thing that we had
+
+1532
+01:05:54,039 --> 01:06:01,200
+above so it it looks like this but over
+
+1533
+01:05:58,200 --> 01:06:03,720
+here we have a clipped version of this
+
+1534
+01:06:01,200 --> 01:06:07,000
+where essentially what we do is we
+
+1535
+01:06:03,720 --> 01:06:07,000
+clip this
+
+1536
+01:06:21,119 --> 01:06:27,880
+ratio this ratio to be within uh a
+
+1537
+01:06:24,720 --> 01:06:32,160
+certain range of the original ratio and
+
+1538
+01:06:27,880 --> 01:06:37,880
+what this is doing is this is
+
+1539
+01:06:32,160 --> 01:06:41,400
+essentially forcing the model to um not
+
+1540
+01:06:37,880 --> 01:06:44,000
+reward large jumps in the space um
+
+1541
+01:06:41,400 --> 01:06:47,559
+because if you take the
+
+1542
+01:06:44,000 --> 01:06:49,160
+minimum and actually I'm I'm sorry I
+
+1543
+01:06:47,559 --> 01:06:50,720
+just realized I I might have done
+
+1544
+01:06:49,160 --> 01:06:52,520
+something confusing here because this is
+
+1545
+01:06:50,720 --> 01:06:53,960
+actually higher as better so this isn't
+
+1546
+01:06:52,520 --> 01:06:56,079
+really a loss function this is something
+
+1547
+01:06:53,960 --> 01:06:57,680
+you're attempting to maximize so
+
+1548
+01:06:56,079 --> 01:06:59,839
+in contrast to all of the other things I
+
+1549
+01:06:57,680 --> 01:07:01,680
+was talking about before um this is
+
+1550
+01:06:59,839 --> 01:07:04,400
+something where higher is better instead
+
+1551
+01:07:01,680 --> 01:07:07,599
+of lower is better but anyway basically
+
+1552
+01:07:04,400 --> 01:07:09,599
+by taking the minimum of this you're
+
+1553
+01:07:07,599 --> 01:07:11,960
+encouraging the model
+
+1554
+01:07:09,599 --> 01:07:16,279
+to
+
+1555
+01:07:11,960 --> 01:07:18,559
+uh keep examining the space where you
+
+1556
+01:07:16,279 --> 01:07:20,799
+don't diverge much from the original
+
+1557
+01:07:18,559 --> 01:07:22,920
+model and if the space where the
+
+1558
+01:07:20,799 --> 01:07:25,240
+original model was in is better than the
+
+1559
+01:07:22,920 --> 01:07:27,440
+new space that your model has moved into
+
+1560
+01:07:25,240 --> 01:07:30,920
+you move back towards the original model
+
+1561
+01:07:27,440 --> 01:07:33,000
+so basically like if you had um if you
+
+1562
+01:07:30,920 --> 01:07:34,960
+learned a model if you started learning
+
+1563
+01:07:33,000 --> 01:07:37,960
+a model that looked like it was
+
+1564
+01:07:34,960 --> 01:07:40,279
+optimizing uh your your reward but then
+
+1565
+01:07:37,960 --> 01:07:43,119
+suddenly the model went off the rails
+
+1566
+01:07:40,279 --> 01:07:45,000
+and um it starts generating completely
+
+1567
+01:07:43,119 --> 01:07:47,319
+nonsense outputs that get really bad
+
+1568
+01:07:45,000 --> 01:07:49,119
+reward this will push it back towards
+
+1569
+01:07:47,319 --> 01:07:50,920
+the original policy and that's the basic
+
+1570
+01:07:49,119 --> 01:07:54,279
+idea behind
+
+1571
+01:07:50,920 --> 01:07:57,640
+P um in terms of what I see people using
+
+1572
+01:07:54,279 --> 01:07:59,799
+um po was like really really popular for
+
+1573
+01:07:57,640 --> 01:08:01,880
+a while but I've started to see people
+
+1574
+01:07:59,799 --> 01:08:04,799
+use alternative strategies that use K
+
+1575
+01:08:01,880 --> 01:08:06,880
+regularization so I don't I don't think
+
+1576
+01:08:04,799 --> 01:08:08,520
+either one of them is like particularly
+
+1577
+01:08:06,880 --> 01:08:10,039
+more popular than any of the others and
+
+1578
+01:08:08,520 --> 01:08:13,720
+this one's a little bit simpler
+
+1579
+01:08:10,039 --> 01:08:13,720
+conceptually so I like the the
+
+1580
+01:08:14,880 --> 01:08:19,279
+one cool um any questions about
+
+1581
+01:08:20,359 --> 01:08:26,759
+this okay um and actually one thing I
+
+1582
+01:08:24,640 --> 01:08:29,679
+should mention is um all of these things
+
+1583
+01:08:26,759 --> 01:08:32,120
+are implemented uh in you know whatever
+
+1584
+01:08:29,679 --> 01:08:33,759
+libraries you use like hugging face TRL
+
+1585
+01:08:32,120 --> 01:08:35,679
+Transformer reinforcement learning as an
+
+1586
+01:08:33,759 --> 01:08:37,040
+example Library all of these methods are
+
+1587
+01:08:35,679 --> 01:08:38,400
+implemented there so if you actually
+
+1588
+01:08:37,040 --> 01:08:40,600
+want to use these in practice that's
+
+1589
+01:08:38,400 --> 01:08:40,600
+good
+
+1590
+01:08:40,839 --> 01:08:46,359
+place the next thing is adding a
+
+1591
+01:08:42,920 --> 01:08:48,679
+Baseline and so the basic idea is that
+
+1592
+01:08:46,359 --> 01:08:52,199
+you have ex expectations about your
+
+1593
+01:08:48,679 --> 01:08:54,640
+reward for a particular sentence and um
+
+1594
+01:08:52,199 --> 01:08:56,560
+like let's say we wanted to uh translate
+
+1595
+01:08:54,640 --> 01:08:58,400
+a sentence and we have uh something like
+
+1596
+01:08:56,560 --> 01:09:01,279
+this is an easy sentence and buffalo
+
+1597
+01:08:58,400 --> 01:09:02,920
+buffalo buffalo which is a harder
+
+1598
+01:09:01,279 --> 01:09:07,799
+sentence to
+
+1599
+01:09:02,920 --> 01:09:09,679
+translate and so we have a reward um if
+
+1600
+01:09:07,799 --> 01:09:11,759
+if you're not familiar with this example
+
+1601
+01:09:09,679 --> 01:09:13,480
+you can search on Wikipedia for buffalo
+
+1602
+01:09:11,759 --> 01:09:16,759
+buffalo buffalo and you'll you'll find
+
+1603
+01:09:13,480 --> 01:09:19,520
+out what I'm talking about um but uh
+
+1604
+01:09:16,759 --> 01:09:21,440
+there's a reward uh and let's say you
+
+1605
+01:09:19,520 --> 01:09:24,359
+got a reward of 0.8 for the first one
+
+1606
+01:09:21,440 --> 01:09:29,679
+and a reward of 0.3 for the second
+
+1607
+01:09:24,359 --> 01:09:31,679
+one but the problem is if um the first
+
+1608
+01:09:29,679 --> 01:09:33,640
+one actually is really easy and the
+
+1609
+01:09:31,679 --> 01:09:36,120
+second one is really hard getting a
+
+1610
+01:09:33,640 --> 01:09:37,799
+reward of 0.8 for the second one for
+
+1611
+01:09:36,120 --> 01:09:40,080
+like a translation or something is
+
+1612
+01:09:37,799 --> 01:09:41,120
+actually bad right and a reward of 0.3
+
+1613
+01:09:40,080 --> 01:09:45,239
+is good because you're moving in the
+
+1614
+01:09:41,120 --> 01:09:49,359
+right direction and so you basically um
+
+1615
+01:09:45,239 --> 01:09:52,239
+you have uh the Baseline uh minus reward
+
+1616
+01:09:49,359 --> 01:09:54,960
+or sorry reward minus Baseline and this
+
+1617
+01:09:52,239 --> 01:09:56,520
+would give you a negative value for this
+
+1618
+01:09:54,960 --> 01:09:59,320
+first one a positive value for the
+
+1619
+01:09:56,520 --> 01:10:01,360
+second one and so the basic idea is can
+
+1620
+01:09:59,320 --> 01:10:04,400
+we predict a priori how difficult this
+
+1621
+01:10:01,360 --> 01:10:05,440
+example is and then uh adjust our reward
+
+1622
+01:10:04,400 --> 01:10:08,360
+based on
+
+1623
+01:10:05,440 --> 01:10:10,960
+that and
+
+1624
+01:10:08,360 --> 01:10:13,679
+so that's the basic idea you just have
+
+1625
+01:10:10,960 --> 01:10:15,560
+kind of like a baseline model um you
+
+1626
+01:10:13,679 --> 01:10:19,320
+have a baseline model that predicts this
+
+1627
+01:10:15,560 --> 01:10:19,320
+and uh you adjust uh
+
+1628
+01:10:19,760 --> 01:10:25,000
+appropriately um there's two major ways
+
+1629
+01:10:22,719 --> 01:10:27,600
+you can do this the first one um the
+
+1630
+01:10:25,000 --> 01:10:29,800
+Baseline doesn't need to be anything um
+
+1631
+01:10:27,600 --> 01:10:32,960
+the only hope is that it decreases the
+
+1632
+01:10:29,800 --> 01:10:35,960
+variance in your reward uh and makes
+
+1633
+01:10:32,960 --> 01:10:38,239
+learning more stable um there's two
+
+1634
+01:10:35,960 --> 01:10:40,159
+options that I see done pretty widely
+
+1635
+01:10:38,239 --> 01:10:43,000
+the first one is predicting the final
+
+1636
+01:10:40,159 --> 01:10:47,360
+reward um predicting the final reward
+
+1637
+01:10:43,000 --> 01:10:50,960
+using a model that doesn't look at
+
+1638
+01:10:47,360 --> 01:10:53,400
+all at the answer that you provided it
+
+1639
+01:10:50,960 --> 01:10:55,880
+only looks at the input or it only looks
+
+1640
+01:10:53,400 --> 01:10:58,840
+at the intermediate States of uh you
+
+1641
+01:10:55,880 --> 01:11:00,480
+know a model or something and so at the
+
+1642
+01:10:58,840 --> 01:11:03,280
+sentence level you can have one Baseline
+
+1643
+01:11:00,480 --> 01:11:04,719
+per sentence um you can also do it at
+
+1644
+01:11:03,280 --> 01:11:10,560
+each decoder
+
+1645
+01:11:04,719 --> 01:11:11,640
+State and this is uh basically you can
+
+1646
+01:11:10,560 --> 01:11:13,040
+do this anytime you're doing
+
+1647
+01:11:11,640 --> 01:11:15,199
+reinforcement learning by just training
+
+1648
+01:11:13,040 --> 01:11:18,199
+a regression model that does this for
+
+1649
+01:11:15,199 --> 01:11:19,679
+you based on the rewards you get the
+
+1650
+01:11:18,199 --> 01:11:21,040
+important thing is the Baseline is not
+
+1651
+01:11:19,679 --> 01:11:22,640
+allowed to use any of your actual
+
+1652
+01:11:21,040 --> 01:11:25,679
+predictions because once you start using
+
+1653
+01:11:22,640 --> 01:11:26,640
+the predictions then um your uh it's not
+
+1654
+01:11:25,679 --> 01:11:28,679
+a
+
+1655
+01:11:26,640 --> 01:11:30,840
+baseline another option which is
+
+1656
+01:11:28,679 --> 01:11:33,440
+relatively easy to implement but can
+
+1657
+01:11:30,840 --> 01:11:36,320
+still be effective is you calculate the
+
+1658
+01:11:33,440 --> 01:11:38,719
+mean of the rewards in a batch and so if
+
+1659
+01:11:36,320 --> 01:11:40,880
+you have a big batch of data and your
+
+1660
+01:11:38,719 --> 01:11:44,440
+average reward in the batch is like
+
+1661
+01:11:40,880 --> 01:11:46,480
+0.4 uh then you just subtract that 0.4
+
+1662
+01:11:44,440 --> 01:11:50,080
+uh and calculate your reward based on
+
+1663
+01:11:46,480 --> 01:11:50,080
+that so that's another option that can
+
+1664
+01:11:51,800 --> 01:11:57,800
+use
+
+1665
+01:11:53,639 --> 01:12:00,000
+um a kind of extreme example of this uh
+
+1666
+01:11:57,800 --> 01:12:01,199
+of creating a baseline is contrasting
+
+1667
+01:12:00,000 --> 01:12:03,639
+pairwise
+
+1668
+01:12:01,199 --> 01:12:05,880
+examples um or
+
+1669
+01:12:03,639 --> 01:12:08,280
+contrasting different outputs for the
+
+1670
+01:12:05,880 --> 01:12:12,040
+same input
+
+1671
+01:12:08,280 --> 01:12:13,920
+and you can easily learn uh directly
+
+1672
+01:12:12,040 --> 01:12:16,239
+from pairwise Human
+
+1673
+01:12:13,920 --> 01:12:18,199
+preferences uh which can provide more
+
+1674
+01:12:16,239 --> 01:12:20,760
+stability because you know one is better
+
+1675
+01:12:18,199 --> 01:12:23,880
+than the other so you essentially can be
+
+1676
+01:12:20,760 --> 01:12:26,199
+sure that uh you're upweighting a better
+
+1677
+01:12:23,880 --> 01:12:29,560
+one and down weting a worse one
+
+1678
+01:12:26,199 --> 01:12:31,400
+um this is the idea behind DPO which is
+
+1679
+01:12:29,560 --> 01:12:33,719
+a recently pretty popular model but
+
+1680
+01:12:31,400 --> 01:12:36,800
+there's also other previous methods that
+
+1681
+01:12:33,719 --> 01:12:40,199
+did similar things and the way DPO works
+
+1682
+01:12:36,800 --> 01:12:45,040
+is it basically calculates this ratio of
+
+1683
+01:12:40,199 --> 01:12:49,280
+uh the probability of the new uh the new
+
+1684
+01:12:45,040 --> 01:12:51,639
+model to the old model but it UPS this
+
+1685
+01:12:49,280 --> 01:12:53,639
+probability for a good output and it
+
+1686
+01:12:51,639 --> 01:12:56,280
+downweights this probability for a bad
+
+1687
+01:12:53,639 --> 01:12:57,679
+output and so
+
+1688
+01:12:56,280 --> 01:13:00,120
+here we have our better outputs over
+
+1689
+01:12:57,679 --> 01:13:02,040
+here here we have our worse outputs and
+
+1690
+01:13:00,120 --> 01:13:03,600
+you just it's basically learning to
+
+1691
+01:13:02,040 --> 01:13:05,639
+upate the probability and downweight
+
+1692
+01:13:03,600 --> 01:13:09,320
+probability
+
+1693
+01:13:05,639 --> 01:13:09,320
+accordingly so
+
+1694
+01:13:09,360 --> 01:13:15,040
+um you can notice that DPO is very
+
+1695
+01:13:12,280 --> 01:13:18,040
+similar to PO um and that it's learning
+
+1696
+01:13:15,040 --> 01:13:19,679
+uh it's using these ratios but the
+
+1697
+01:13:18,040 --> 01:13:21,520
+disadvantage of this is you obviously
+
+1698
+01:13:19,679 --> 01:13:23,120
+require pairwise judgments and you can't
+
+1699
+01:13:21,520 --> 01:13:26,120
+learn a model if you don't have these
+
+1700
+01:13:23,120 --> 01:13:28,080
+pawise judgments so
+
+1701
+01:13:26,120 --> 01:13:30,760
+the
+
+1702
+01:13:28,080 --> 01:13:33,159
+beta yeah so the beta term is is
+
+1703
+01:13:30,760 --> 01:13:35,840
+basically a normalization term it's a
+
+1704
+01:13:33,159 --> 01:13:39,960
+hyper parameter um
+
+1705
+01:13:35,840 --> 01:13:41,840
+for DPO sorry I read the paper right
+
+1706
+01:13:39,960 --> 01:13:43,639
+when it came out and I don't remember if
+
+1707
+01:13:41,840 --> 01:13:45,600
+it's a direct derivation from the K
+
+1708
+01:13:43,639 --> 01:13:47,960
+Divergence term or not but I think it
+
+1709
+01:13:45,600 --> 01:13:49,800
+might be um I'd have to go back and look
+
+1710
+01:13:47,960 --> 01:13:50,480
+at the look at the paper but basically
+
+1711
+01:13:49,800 --> 01:13:53,600
+the
+
+1712
+01:13:50,480 --> 01:13:56,760
+more the larger this is the larger
+
+1713
+01:13:53,600 --> 01:13:59,320
+gradient steps you'll be
+
+1714
+01:13:56,760 --> 01:14:00,639
+it also um like you'll notice there
+
+1715
+01:13:59,320 --> 01:14:03,400
+sorry I didn't mention this but you'll
+
+1716
+01:14:00,639 --> 01:14:06,120
+notice there's a sigmoid term here so
+
+1717
+01:14:03,400 --> 01:14:09,000
+the the
+
+1718
+01:14:06,120 --> 01:14:10,080
+beta the larger you increase the beta
+
+1719
+01:14:09,000 --> 01:14:13,239
+the
+
+1720
+01:14:10,080 --> 01:14:16,600
+more small differences in these
+
+1721
+01:14:13,239 --> 01:14:18,719
+values like it basically like stretches
+
+1722
+01:14:16,600 --> 01:14:22,280
+or shrinks the sigmoid with respect to
+
+1723
+01:14:18,719 --> 01:14:24,120
+how beak the it is so it will um it will
+
+1724
+01:14:22,280 --> 01:14:25,800
+affect how much like small differences
+
+1725
+01:14:24,120 --> 01:14:27,960
+in this will affect
+
+1726
+01:14:25,800 --> 01:14:30,120
+but I I think this was derived from the
+
+1727
+01:14:27,960 --> 01:14:31,760
+K regularization term that we had
+
+1728
+01:14:30,120 --> 01:14:34,400
+previously in
+
+1729
+01:14:31,760 --> 01:14:35,800
+um in this slide here but I have to go
+
+1730
+01:14:34,400 --> 01:14:40,520
+back and double check unless somebody
+
+1731
+01:14:35,800 --> 01:14:43,239
+knows it is okay good yeah
+
+1732
+01:14:40,520 --> 01:14:45,000
+so I don't want to say wrong things but
+
+1733
+01:14:43,239 --> 01:14:48,239
+I also don't want
+
+1734
+01:14:45,000 --> 01:14:50,920
+to okay cool um and so then increasing
+
+1735
+01:14:48,239 --> 01:14:55,080
+batch size
+
+1736
+01:14:50,920 --> 01:14:57,360
+um because each uh another thing is um
+
+1737
+01:14:55,080 --> 01:14:58,440
+kind of NE necessarily reinforcement
+
+1738
+01:14:57,360 --> 01:14:59,920
+learning is going to have higher
+
+1739
+01:14:58,440 --> 01:15:01,400
+variance and maximum likelihood
+
+1740
+01:14:59,920 --> 01:15:04,199
+estimation just because we're doing samp
+
+1741
+01:15:01,400 --> 01:15:07,840
+playing and other things like this and
+
+1742
+01:15:04,199 --> 01:15:09,440
+um so one very simple thing you can do
+
+1743
+01:15:07,840 --> 01:15:11,280
+is just increase the number of examples
+
+1744
+01:15:09,440 --> 01:15:13,679
+or rollouts that you do before an update
+
+1745
+01:15:11,280 --> 01:15:15,800
+to stabilize and so I I would definitely
+
+1746
+01:15:13,679 --> 01:15:17,480
+suggest that if you're seeing any
+
+1747
+01:15:15,800 --> 01:15:18,679
+stability after doing all of the tricks
+
+1748
+01:15:17,480 --> 01:15:20,400
+that I mentioned before that you
+
+1749
+01:15:18,679 --> 01:15:23,040
+increase your batch size and often that
+
+1750
+01:15:20,400 --> 01:15:25,480
+can just resolve your problems
+
+1751
+01:15:23,040 --> 01:15:28,760
+um another uh
+
+1752
+01:15:25,480 --> 01:15:30,560
+thing that people often do is um save
+
+1753
+01:15:28,760 --> 01:15:32,040
+many many previous rollouts because
+
+1754
+01:15:30,560 --> 01:15:34,199
+generally doing rollouts is more
+
+1755
+01:15:32,040 --> 01:15:37,840
+expensive doing rollouts and collecting
+
+1756
+01:15:34,199 --> 01:15:39,560
+rewards is more expensive and so um you
+
+1757
+01:15:37,840 --> 01:15:42,360
+can save the roll outs that you have
+
+1758
+01:15:39,560 --> 01:15:43,840
+done before and uh keep them around so
+
+1759
+01:15:42,360 --> 01:15:46,600
+you can update parameters with larger
+
+1760
+01:15:43,840 --> 01:15:50,800
+batches in a more efficient
+
+1761
+01:15:46,600 --> 01:15:53,120
+way cool so that's all I have uh I just
+
+1762
+01:15:50,800 --> 01:15:54,400
+realized we're exactly at time so uh I
+
+1763
+01:15:53,120 --> 01:15:56,440
+should finish up here but I'll be happy
+
+1764
+01:15:54,400 --> 01:15:59,440
+to take any
+
+1765
+01:15:56,440 --> 01:15:59,440
+for
+
+1766
+01:16:01,679 --> 01:16:04,679
+thanks
\ No newline at end of file