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+WEBVTT
+
+00:00:00.840 --> 00:00:05.920
+okay so uh let's get started um today
+
+00:00:04.200 --> 00:00:08.000
+I'm going to be talking about learning
+
+00:00:05.920 --> 00:00:09.480
+from Human feedback I wrote
+
+00:00:08.000 --> 00:00:12.160
+reinforcement learning from Human
+
+00:00:09.480 --> 00:00:14.519
+feedback because that's what um you know
+
+00:00:12.160 --> 00:00:15.759
+a lot of people talk about nowadays but
+
+00:00:14.519 --> 00:00:18.880
+actually there's other methods of
+
+00:00:15.759 --> 00:00:21.840
+learning from Human feedback so first
+
+00:00:18.880 --> 00:00:24.760
+I'm going to be talking about the ways
+
+00:00:21.840 --> 00:00:27.920
+we can get uh human feedback for the
+
+00:00:24.760 --> 00:00:31.039
+generations of models and mostly focus
+
+00:00:27.920 --> 00:00:32.960
+on generation tasks because is um
+
+00:00:31.039 --> 00:00:35.800
+generation tasks are harder than like
+
+00:00:32.960 --> 00:00:38.559
+classification tasks that we uh we deal
+
+00:00:35.800 --> 00:00:40.000
+with normally so I'll spend a fair
+
+00:00:38.559 --> 00:00:42.239
+amount of time talking about how we do
+
+00:00:40.000 --> 00:00:45.760
+that and then after I talk about how we
+
+00:00:42.239 --> 00:00:48.360
+do that we'll move into um how we
+
+00:00:45.760 --> 00:00:51.160
+actually learn from that
+
+00:00:48.360 --> 00:00:53.399
+signal so normally what we've done up
+
+00:00:51.160 --> 00:00:56.399
+until this point is maximum likelihood
+
+00:00:53.399 --> 00:00:58.199
+training uh this is just an overview
+
+00:00:56.399 --> 00:00:59.559
+slide so we what we want to do is we
+
+00:00:58.199 --> 00:01:00.760
+want to maximize the likelihood of
+
+00:00:59.559 --> 00:01:03.280
+predicting the next word and the
+
+00:01:00.760 --> 00:01:05.960
+reference given the previous words uh
+
+00:01:03.280 --> 00:01:08.119
+which gives us the loss of the output
+
+00:01:05.960 --> 00:01:09.799
+given the input uh where you know the
+
+00:01:08.119 --> 00:01:13.960
+input can be the prompt the output can
+
+00:01:09.799 --> 00:01:16.080
+be the answer to uh the output but
+
+00:01:13.960 --> 00:01:18.360
+there's uh lots of problems with
+
+00:01:16.080 --> 00:01:20.439
+learning from Maximum likelihood and I'm
+
+00:01:18.360 --> 00:01:22.079
+going to give three examples here I
+
+00:01:20.439 --> 00:01:24.159
+think all of these are actually real
+
+00:01:22.079 --> 00:01:26.880
+problems uh that we need to be worried
+
+00:01:24.159 --> 00:01:30.240
+about so the first one is that some
+
+00:01:26.880 --> 00:01:32.439
+mistakes are worse than others so um in
+
+00:01:30.240 --> 00:01:33.560
+the end we want good outputs and some
+
+00:01:32.439 --> 00:01:36.520
+mistaken
+
+00:01:33.560 --> 00:01:38.200
+predictions uh can be a bigger problem
+
+00:01:36.520 --> 00:01:42.680
+for the output being
+
+00:01:38.200 --> 00:01:46.000
+good so to give an example uh let's say
+
+00:01:42.680 --> 00:01:47.600
+what we actually wanted from like a
+
+00:01:46.000 --> 00:01:49.320
+speech recognition system or a
+
+00:01:47.600 --> 00:01:54.040
+translation system or something like
+
+00:01:49.320 --> 00:01:54.040
+that is uh please send this package to
+
+00:01:54.280 --> 00:01:58.920
+Pittsburgh if I write please send a
+
+00:01:56.880 --> 00:02:01.560
+package to Pittsburgh then this is not a
+
+00:01:58.920 --> 00:02:03.560
+huge problem
+
+00:02:01.560 --> 00:02:06.479
+if I write uh please send this package
+
+00:02:03.560 --> 00:02:07.719
+to Tokyo then that might be a big
+
+00:02:06.479 --> 00:02:09.640
+problem because the package you wanted
+
+00:02:07.719 --> 00:02:12.760
+to come to Pittsburgh goes to Tokyo
+
+00:02:09.640 --> 00:02:13.680
+instead and uh you might not want that
+
+00:02:12.760 --> 00:02:16.080
+to
+
+00:02:13.680 --> 00:02:18.000
+happen you might also have it say
+
+00:02:16.080 --> 00:02:20.400
+bleeping send this package to Pittsburgh
+
+00:02:18.000 --> 00:02:22.200
+instead of pleas um and that would be a
+
+00:02:20.400 --> 00:02:24.200
+problem in a customer service system
+
+00:02:22.200 --> 00:02:28.400
+right because your customer would uh
+
+00:02:24.200 --> 00:02:28.400
+leave and never come back
+
+00:02:28.840 --> 00:02:32.040
+so
+
+00:02:30.360 --> 00:02:33.720
+determiner like this is not going to
+
+00:02:32.040 --> 00:02:35.640
+cause a huge issue U messing up other
+
+00:02:33.720 --> 00:02:37.519
+things is going to cause a larger
+
+00:02:35.640 --> 00:02:39.519
+issue but from the point of view of
+
+00:02:37.519 --> 00:02:42.680
+Maximum likelihood all of these are just
+
+00:02:39.519 --> 00:02:44.560
+tokens and messing up one token is the
+
+00:02:42.680 --> 00:02:47.519
+same as messing up another token so
+
+00:02:44.560 --> 00:02:50.040
+that's uh you know an
+
+00:02:47.519 --> 00:02:52.080
+issue another problem is that the gold
+
+00:02:50.040 --> 00:02:54.640
+standard and maximum likelihood
+
+00:02:52.080 --> 00:02:57.480
+estimation can be bad it can be like not
+
+00:02:54.640 --> 00:02:59.239
+what you want and uh corpa are full of
+
+00:02:57.480 --> 00:03:02.400
+outputs that we wouldn't want a language
+
+00:02:59.239 --> 00:03:05.400
+model producing so for example uh toxic
+
+00:03:02.400 --> 00:03:07.799
+comments on Reddit uh
+
+00:03:05.400 --> 00:03:09.959
+disinformation um another thing that a
+
+00:03:07.799 --> 00:03:13.000
+lot of people don't think about uh quite
+
+00:03:09.959 --> 00:03:15.640
+as much is a lot of the data online is
+
+00:03:13.000 --> 00:03:17.680
+uh from is automatically generated
+
+00:03:15.640 --> 00:03:19.720
+nowadays for example from machine
+
+00:03:17.680 --> 00:03:24.080
+translation a lot of the translations
+
+00:03:19.720 --> 00:03:25.720
+online are from uh 2016 Google translate
+
+00:03:24.080 --> 00:03:27.560
+uh when Google translate was a lot less
+
+00:03:25.720 --> 00:03:29.120
+good than it is now and so you have like
+
+00:03:27.560 --> 00:03:31.760
+poor quality translations that were
+
+00:03:29.120 --> 00:03:31.760
+automatically
+
+00:03:33.040 --> 00:03:37.959
+a final problem is uh something that's
+
+00:03:35.280 --> 00:03:40.360
+called exposure bias and exposure bias
+
+00:03:37.959 --> 00:03:44.000
+basically what it means is mle training
+
+00:03:40.360 --> 00:03:46.000
+doesn't consider um the necessarity the
+
+00:03:44.000 --> 00:03:48.599
+necessity for generation and it relies
+
+00:03:46.000 --> 00:03:51.360
+on gold standard context so if we go
+
+00:03:48.599 --> 00:03:54.159
+back to the mle equation when we're
+
+00:03:51.360 --> 00:03:57.200
+calculating mle this y less than T is
+
+00:03:54.159 --> 00:03:59.200
+always correct it's always a good output
+
+00:03:57.200 --> 00:04:01.439
+and so what the model does is it learns
+
+00:03:59.200 --> 00:04:04.280
+to over rely on good
+
+00:04:01.439 --> 00:04:06.079
+outputs and one example of a problem
+
+00:04:04.280 --> 00:04:08.360
+that this causes is models tend to
+
+00:04:06.079 --> 00:04:10.560
+repeat themselves over and over again
+
+00:04:08.360 --> 00:04:12.319
+for example um when you use some
+
+00:04:10.560 --> 00:04:15.079
+generation algorithms and the reason why
+
+00:04:12.319 --> 00:04:18.519
+this happens is because in a gold
+
+00:04:15.079 --> 00:04:22.079
+standard output if a word has appeared
+
+00:04:18.519 --> 00:04:25.840
+previously that word is more likely to
+
+00:04:22.079 --> 00:04:28.560
+happen next so like if you say um like I
+
+00:04:25.840 --> 00:04:29.759
+am going um I am going to Pittsburgh
+
+00:04:28.560 --> 00:04:31.880
+you're much more likely to say
+
+00:04:29.759 --> 00:04:33.000
+Pittsburgh again in the future because
+
+00:04:31.880 --> 00:04:35.720
+you're talking about Pittsburgh
+
+00:04:33.000 --> 00:04:37.400
+topically as coherent so what you get is
+
+00:04:35.720 --> 00:04:38.639
+you get mle trained models saying I'm
+
+00:04:37.400 --> 00:04:40.160
+going to Pittsburgh I am going to
+
+00:04:38.639 --> 00:04:41.680
+Pittsburgh I am going to Pittsburgh I
+
+00:04:40.160 --> 00:04:45.280
+going to Pittsburgh you've probably seen
+
+00:04:41.680 --> 00:04:47.320
+this before uh at some point and so um
+
+00:04:45.280 --> 00:04:49.320
+exposure bias is basically that the
+
+00:04:47.320 --> 00:04:51.039
+model has never been exposed to mistakes
+
+00:04:49.320 --> 00:04:55.240
+in the past and so it can't deal with
+
+00:04:51.039 --> 00:04:56.840
+them so what this does is um if you have
+
+00:04:55.240 --> 00:04:58.560
+an alternative training algorithm you
+
+00:04:56.840 --> 00:05:02.120
+can fix this by generating a whole bunch
+
+00:04:58.560 --> 00:05:04.880
+of outputs uh down like scoring some of
+
+00:05:02.120 --> 00:05:06.880
+them poorly and penalizing the model for
+
+00:05:04.880 --> 00:05:09.960
+uh generating po outputs and so that can
+
+00:05:06.880 --> 00:05:09.960
+fix these problems as
+
+00:05:10.800 --> 00:05:18.440
+well uh any questions about this all
+
+00:05:15.199 --> 00:05:20.800
+good Okay cool so now I'd like to get
+
+00:05:18.440 --> 00:05:23.919
+into how we measure how good an output
+
+00:05:20.800 --> 00:05:26.360
+is and there's different ways of doing
+
+00:05:23.919 --> 00:05:30.319
+this um the first one is objective
+
+00:05:26.360 --> 00:05:32.680
+assessment so for some uh tasks or for
+
+00:05:30.319 --> 00:05:35.400
+many tasks there's kind of objectively a
+
+00:05:32.680 --> 00:05:37.280
+correct answer there's also human
+
+00:05:35.400 --> 00:05:40.360
+subjective annotations so you can ask
+
+00:05:37.280 --> 00:05:42.919
+humans to do annotation for you there's
+
+00:05:40.360 --> 00:05:45.400
+machine prediction of human
+
+00:05:42.919 --> 00:05:48.319
+preferences and there's also use in
+
+00:05:45.400 --> 00:05:50.840
+another system in a downstream
+
+00:05:48.319 --> 00:05:52.960
+task so the way objective assessment
+
+00:05:50.840 --> 00:05:54.919
+works is you have an annotated correct
+
+00:05:52.960 --> 00:05:57.080
+answer in match against this so like if
+
+00:05:54.919 --> 00:06:00.600
+you're solving math problems uh
+
+00:05:57.080 --> 00:06:02.560
+answering objective questions and and
+
+00:06:00.600 --> 00:06:04.280
+you know you can pick any arbitrary
+
+00:06:02.560 --> 00:06:06.840
+example you can pick your classification
+
+00:06:04.280 --> 00:06:09.800
+example from uh like your text
+
+00:06:06.840 --> 00:06:11.880
+classification tasks an even clearer
+
+00:06:09.800 --> 00:06:13.880
+example is if you have math problems
+
+00:06:11.880 --> 00:06:15.639
+there's kind of objectively one answer
+
+00:06:13.880 --> 00:06:18.080
+to any math problem and there's no other
+
+00:06:15.639 --> 00:06:19.680
+answer that could be correct so this
+
+00:06:18.080 --> 00:06:21.160
+makes your life easy if you're handling
+
+00:06:19.680 --> 00:06:22.560
+this type of problem but of course
+
+00:06:21.160 --> 00:06:24.120
+there's many other types of problems we
+
+00:06:22.560 --> 00:06:26.039
+want to handle that don't have objective
+
+00:06:24.120 --> 00:06:29.039
+answers like
+
+00:06:26.039 --> 00:06:31.440
+this so let's say we're handling a gener
+
+00:06:29.039 --> 00:06:34.680
+a generation task where we don't have an
+
+00:06:31.440 --> 00:06:36.360
+objective answer um in this Cas kind of
+
+00:06:34.680 --> 00:06:39.440
+one of our gold standards is human
+
+00:06:36.360 --> 00:06:42.360
+evaluation so we might have a source
+
+00:06:39.440 --> 00:06:44.919
+input like a prompt or an input text for
+
+00:06:42.360 --> 00:06:47.240
+machine translation we have one or
+
+00:06:44.919 --> 00:06:49.960
+several hypotheses and we ask a human
+
+00:06:47.240 --> 00:06:53.280
+annotator to basically give uh a score
+
+00:06:49.960 --> 00:06:55.759
+for them or do some sort of other
+
+00:06:53.280 --> 00:06:59.759
+annotation and the different varieties
+
+00:06:55.759 --> 00:07:03.080
+of annotation that we can give are um
+
+00:06:59.759 --> 00:07:04.599
+something called direct assessment so uh
+
+00:07:03.080 --> 00:07:06.599
+direct assessment is a term that comes
+
+00:07:04.599 --> 00:07:09.280
+from machine translation uh so you might
+
+00:07:06.599 --> 00:07:11.039
+not see it used uh lots of other places
+
+00:07:09.280 --> 00:07:13.120
+but it's basically just give a score
+
+00:07:11.039 --> 00:07:15.759
+directly to how good the output is so
+
+00:07:13.120 --> 00:07:17.199
+you can say like if you say please send
+
+00:07:15.759 --> 00:07:18.960
+this translation is please send this
+
+00:07:17.199 --> 00:07:21.759
+package to Tokyo we give it a score of
+
+00:07:18.960 --> 00:07:24.360
+two out of 10 or something like
+
+00:07:21.759 --> 00:07:28.000
+this
+
+00:07:24.360 --> 00:07:30.840
+so the the question here is like what
+
+00:07:28.000 --> 00:07:32.400
+does like let's say I gave a score of
+
+00:07:30.840 --> 00:07:34.520
+two out of 10 for please send this
+
+00:07:32.400 --> 00:07:37.680
+package to Tokyo what score should I
+
+00:07:34.520 --> 00:07:40.240
+give for please send a package to Tokyo
+
+00:07:37.680 --> 00:07:42.360
+anyone have any ideas the the correct
+
+00:07:40.240 --> 00:07:46.520
+answer is please send this package to
+
+00:07:42.360 --> 00:07:48.000
+take out of eight out of 10 yeah but you
+
+00:07:46.520 --> 00:07:50.440
+might disagree on that right it's kind
+
+00:07:48.000 --> 00:07:52.159
+of like subjective um one of the
+
+00:07:50.440 --> 00:07:54.039
+difficulties of direct assessment is
+
+00:07:52.159 --> 00:07:55.520
+giving a number like this is pretty
+
+00:07:54.039 --> 00:07:57.800
+difficult if you don't have a very clear
+
+00:07:55.520 --> 00:07:59.720
+rubric and very skilled annotators and
+
+00:07:57.800 --> 00:08:02.879
+it's hard to get consistency between
+
+00:07:59.720 --> 00:08:04.400
+people when you do this so the advantage
+
+00:08:02.879 --> 00:08:05.599
+is it kind of gives you an idea of how
+
+00:08:04.400 --> 00:08:07.520
+good things are overall but the
+
+00:08:05.599 --> 00:08:09.280
+disadvantage is it's more difficult to
+
+00:08:07.520 --> 00:08:11.319
+annotate and get
+
+00:08:09.280 --> 00:08:13.159
+consistency um another thing that I
+
+00:08:11.319 --> 00:08:15.319
+should point out is often scores are
+
+00:08:13.159 --> 00:08:18.680
+assigned separately based on desirable
+
+00:08:15.319 --> 00:08:20.960
+traits so um we don't necessarily just
+
+00:08:18.680 --> 00:08:23.479
+say how good is it we say how fluent is
+
+00:08:20.960 --> 00:08:26.120
+it like is it fluent uh
+
+00:08:23.479 --> 00:08:28.159
+English in Translation there's a concept
+
+00:08:26.120 --> 00:08:30.720
+called adequacy which is how well does
+
+00:08:28.159 --> 00:08:34.599
+the output reflect the input
+
+00:08:30.720 --> 00:08:36.519
+semantics um and if you're assessing
+
+00:08:34.599 --> 00:08:38.440
+translation systems actually it's common
+
+00:08:36.519 --> 00:08:40.519
+to assess fluency without even looking
+
+00:08:38.440 --> 00:08:43.200
+at the input because then you can just
+
+00:08:40.519 --> 00:08:44.880
+say how fluent is it but for adequacy
+
+00:08:43.200 --> 00:08:46.320
+you definitely need to understand the
+
+00:08:44.880 --> 00:08:49.600
+input so you need to be a bilingual
+
+00:08:46.320 --> 00:08:54.680
+speaker to be able to assess
+
+00:08:49.600 --> 00:08:57.560
+that um factuality um and so factuality
+
+00:08:54.680 --> 00:09:00.160
+is tricky um it can either be factuality
+
+00:08:57.560 --> 00:09:03.880
+grounded in a particular input text in
+
+00:09:00.160 --> 00:09:05.600
+which case um the facts would have to be
+
+00:09:03.880 --> 00:09:07.680
+you know things that were said in the
+
+00:09:05.600 --> 00:09:09.399
+input or it can be just kind of is the
+
+00:09:07.680 --> 00:09:11.120
+statement factual in general in which
+
+00:09:09.399 --> 00:09:13.720
+case you need to go online you need to
+
+00:09:11.120 --> 00:09:16.480
+search for things and like uh check
+
+00:09:13.720 --> 00:09:18.480
+whether the statement is factual or not
+
+00:09:16.480 --> 00:09:20.480
+um other things are like coherence does
+
+00:09:18.480 --> 00:09:21.480
+the output fit coherently within the
+
+00:09:20.480 --> 00:09:23.680
+larger
+
+00:09:21.480 --> 00:09:25.680
+discs um and there's many many other
+
+00:09:23.680 --> 00:09:28.120
+ones of these this is also task
+
+00:09:25.680 --> 00:09:29.760
+dependent so like the things you will
+
+00:09:28.120 --> 00:09:31.000
+evaluate for machine transl are
+
+00:09:29.760 --> 00:09:32.880
+different than the ones you would do for
+
+00:09:31.000 --> 00:09:35.760
+dialog which are different than the ones
+
+00:09:32.880 --> 00:09:38.200
+you would do for a general purpose
+
+00:09:35.760 --> 00:09:41.279
+chatot uh which is different kind things
+
+00:09:38.200 --> 00:09:44.120
+you would do for um summarization for
+
+00:09:41.279 --> 00:09:46.320
+example so if you're interested in doing
+
+00:09:44.120 --> 00:09:47.519
+something like this uh then I definitely
+
+00:09:46.320 --> 00:09:48.800
+encourage you to look at what other
+
+00:09:47.519 --> 00:09:51.399
+people have done for the tasks you're
+
+00:09:48.800 --> 00:09:53.079
+interested in uh previously and uh find
+
+00:09:51.399 --> 00:09:54.880
+out the different types of traits that
+
+00:09:53.079 --> 00:09:58.320
+did
+
+00:09:54.880 --> 00:10:00.760
+last uh any any questions about this
+
+00:09:58.320 --> 00:10:03.079
+also
+
+00:10:00.760 --> 00:10:06.920
+okay the next type of feedback is
+
+00:10:03.079 --> 00:10:09.839
+preference ratings um and so this is uh
+
+00:10:06.920 --> 00:10:12.600
+basically what you do is you have two or
+
+00:10:09.839 --> 00:10:14.240
+more outputs from different models or
+
+00:10:12.600 --> 00:10:16.440
+different Generations from an individual
+
+00:10:14.240 --> 00:10:18.839
+model and you ask a human which one is
+
+00:10:16.440 --> 00:10:22.320
+better like is one better than the other
+
+00:10:18.839 --> 00:10:23.839
+or are they tied and so in this case um
+
+00:10:22.320 --> 00:10:26.320
+you might have please send this package
+
+00:10:23.839 --> 00:10:28.880
+to Tokyo please send a package to
+
+00:10:26.320 --> 00:10:31.040
+Tokyo we might disagree on how like good
+
+00:10:28.880 --> 00:10:33.959
+or bad each of them are but I think most
+
+00:10:31.040 --> 00:10:35.959
+people would agree that this one is like
+
+00:10:33.959 --> 00:10:37.480
+despite the fact that it got this wrong
+
+00:10:35.959 --> 00:10:40.160
+the second one is better than the first
+
+00:10:37.480 --> 00:10:42.240
+one so this is a little bit of an easier
+
+00:10:40.160 --> 00:10:45.040
+task it's easier to uh get people to
+
+00:10:42.240 --> 00:10:46.839
+annotate these things
+
+00:10:45.040 --> 00:10:50.519
+consistently however it has the
+
+00:10:46.839 --> 00:10:52.839
+disadvantage that you can't really tell
+
+00:10:50.519 --> 00:10:55.360
+uh whether systems are really good or
+
+00:10:52.839 --> 00:10:57.200
+really bad so let's say you have a bunch
+
+00:10:55.360 --> 00:11:00.279
+of really bad systems that you're
+
+00:10:57.200 --> 00:11:01.839
+comparing with each other um you might
+
+00:11:00.279 --> 00:11:03.680
+find that one is better than the other
+
+00:11:01.839 --> 00:11:06.000
+but that still doesn't mean it's ready
+
+00:11:03.680 --> 00:11:07.399
+to be deployed or if you have a bunch of
+
+00:11:06.000 --> 00:11:11.040
+really good systems they're all
+
+00:11:07.399 --> 00:11:13.000
+basically you know very very similar to
+
+00:11:11.040 --> 00:11:14.399
+another but one is like slightly more
+
+00:11:13.000 --> 00:11:18.639
+fluent than the other you might still
+
+00:11:14.399 --> 00:11:20.680
+get a similar result um and so that also
+
+00:11:18.639 --> 00:11:22.760
+makes it uh you know a little bit
+
+00:11:20.680 --> 00:11:24.880
+difficult to use practically in some
+
+00:11:22.760 --> 00:11:27.040
+ways I didn't put it on the slide but
+
+00:11:24.880 --> 00:11:30.680
+there's another way you can kind of get
+
+00:11:27.040 --> 00:11:33.920
+the best of both worlds um which is a
+
+00:11:30.680 --> 00:11:35.560
+side by side assessment and side by-side
+
+00:11:33.920 --> 00:11:38.440
+assessment basically what you would do
+
+00:11:35.560 --> 00:11:40.560
+is you would say um please send this
+
+00:11:38.440 --> 00:11:43.399
+package to Tokyo please send a package
+
+00:11:40.560 --> 00:11:47.279
+to Pittsburgh give each of them a direct
+
+00:11:43.399 --> 00:11:48.839
+score um but you can use decimal places
+
+00:11:47.279 --> 00:11:51.120
+and you can't use the same score for all
+
+00:11:48.839 --> 00:11:55.920
+of them and so it's
+
+00:11:51.120 --> 00:11:57.480
+like five 500 and 4.99 out of five or
+
+00:11:55.920 --> 00:11:59.519
+something like that like you like one
+
+00:11:57.480 --> 00:12:02.639
+slightly better than the other or or
+
+00:11:59.519 --> 00:12:04.480
+something like that um so there are ways
+
+00:12:02.639 --> 00:12:07.240
+to kind of get Best of Both Worlds if
+
+00:12:04.480 --> 00:12:11.720
+you're interested in doing
+
+00:12:07.240 --> 00:12:11.720
+that um
+
+00:12:14.920 --> 00:12:20.519
+so one problem one other problem with
+
+00:12:18.279 --> 00:12:22.519
+preference rankings is that there's a
+
+00:12:20.519 --> 00:12:24.440
+limited number of things that humans can
+
+00:12:22.519 --> 00:12:28.160
+compare before they get really
+
+00:12:24.440 --> 00:12:32.360
+overwhelmed so if you say I
+
+00:12:28.160 --> 00:12:35.560
+want like I want to
+
+00:12:32.360 --> 00:12:36.920
+rate 15 systems or 20 systems with
+
+00:12:35.560 --> 00:12:39.120
+respect to how good they are with
+
+00:12:36.920 --> 00:12:40.639
+respect to each other it's going to be
+
+00:12:39.120 --> 00:12:43.680
+impossible for humans to come up with a
+
+00:12:40.639 --> 00:12:46.959
+good preference ranking between them and
+
+00:12:43.680 --> 00:12:49.480
+so the typical way around this um which
+
+00:12:46.959 --> 00:12:52.360
+is also used in uh things like the
+
+00:12:49.480 --> 00:12:55.440
+chatbot Arena by lmis and other things
+
+00:12:52.360 --> 00:12:58.720
+like this is to use uh something like an
+
+00:12:55.440 --> 00:13:00.959
+ELO or true skill ranking and what these
+
+00:12:58.720 --> 00:13:03.079
+are is these are things that were
+
+00:13:00.959 --> 00:13:05.760
+created for the ranking of like chess
+
+00:13:03.079 --> 00:13:09.160
+players or video game players or other
+
+00:13:05.760 --> 00:13:11.720
+things where they like b battle against
+
+00:13:09.160 --> 00:13:13.920
+each other in multiple matches uh
+
+00:13:11.720 --> 00:13:16.440
+pair-wise and then you put all of the
+
+00:13:13.920 --> 00:13:18.399
+wins and losses into these ranking
+
+00:13:16.440 --> 00:13:20.600
+algorithms and they give you a score
+
+00:13:18.399 --> 00:13:22.920
+about how good like each of the each of
+
+00:13:20.600 --> 00:13:27.079
+the players are so if you do something
+
+00:13:22.920 --> 00:13:29.480
+like this you can um get basically a
+
+00:13:27.079 --> 00:13:32.120
+ranking of systems despite the that you
+
+00:13:29.480 --> 00:13:35.240
+only did pairwise assessments so these
+
+00:13:32.120 --> 00:13:35.240
+are also a good thing to know
+
+00:13:37.399 --> 00:13:43.839
+about a final variety of human feedback
+
+00:13:40.600 --> 00:13:45.320
+uh that we create is uh air annotation
+
+00:13:43.839 --> 00:13:47.519
+and this can be useful for a number of
+
+00:13:45.320 --> 00:13:49.839
+reasons um but basically the way it
+
+00:13:47.519 --> 00:13:53.839
+works is you annotate individual errors
+
+00:13:49.839 --> 00:13:55.639
+within the outputs and um oh one thing I
+
+00:13:53.839 --> 00:13:58.120
+should mention is that um I'm giving a
+
+00:13:55.639 --> 00:14:00.880
+lot of examples from machine translation
+
+00:13:58.120 --> 00:14:02.800
+um I feel like machine translation has
+
+00:14:00.880 --> 00:14:04.519
+been doing evaluation of generated
+
+00:14:02.800 --> 00:14:07.600
+outputs for a lot longer than a lot of
+
+00:14:04.519 --> 00:14:09.000
+other uh fields of NLP have and
+
+00:14:07.600 --> 00:14:11.800
+therefore their methodology is more
+
+00:14:09.000 --> 00:14:13.480
+developed than a lot of other fields um
+
+00:14:11.800 --> 00:14:16.199
+but a lot of these things can also be
+
+00:14:13.480 --> 00:14:18.079
+applied to uh other uh other tasks as
+
+00:14:16.199 --> 00:14:19.079
+well but anyway getting back to this
+
+00:14:18.079 --> 00:14:20.680
+there's something for machine
+
+00:14:19.079 --> 00:14:23.639
+translation called multi-dimensional
+
+00:14:20.680 --> 00:14:26.240
+quality metrics and the multidimensional
+
+00:14:23.639 --> 00:14:29.160
+quality metrics basically what they do
+
+00:14:26.240 --> 00:14:32.199
+is they annotate spans in the output
+
+00:14:29.160 --> 00:14:34.800
+where each Span in the output is given a
+
+00:14:32.199 --> 00:14:38.079
+severity ranking of the error and it's
+
+00:14:34.800 --> 00:14:40.199
+given a type of the error and there's
+
+00:14:38.079 --> 00:14:42.600
+about eight different types of Errors
+
+00:14:40.199 --> 00:14:44.839
+like this doesn't violate or this
+
+00:14:42.600 --> 00:14:47.399
+violates linguistic conventions of using
+
+00:14:44.839 --> 00:14:49.880
+the word this instead of uh here by
+
+00:14:47.399 --> 00:14:51.639
+using the word uh instead of this here
+
+00:14:49.880 --> 00:14:55.079
+and then this is an accuracy error
+
+00:14:51.639 --> 00:14:57.839
+because it's not accurately con uh uh
+
+00:14:55.079 --> 00:15:01.720
+conveying the output and then this error
+
+00:14:57.839 --> 00:15:04.600
+is minor uh this error is Major um and
+
+00:15:01.720 --> 00:15:06.399
+then there's also like severe severe
+
+00:15:04.600 --> 00:15:07.440
+versus major but minor and major is a
+
+00:15:06.399 --> 00:15:09.680
+more important
+
+00:15:07.440 --> 00:15:11.839
+distinction um so the advantage of this
+
+00:15:09.680 --> 00:15:14.279
+is a couple fold number one it gives you
+
+00:15:11.839 --> 00:15:16.440
+more fine grained feedback uh in that
+
+00:15:14.279 --> 00:15:19.199
+you can say okay this system has a lot
+
+00:15:16.440 --> 00:15:22.199
+of uh accuracy errors this system has a
+
+00:15:19.199 --> 00:15:24.880
+lot of linguistic conventions errors um
+
+00:15:22.199 --> 00:15:28.600
+it also can be more consistent because
+
+00:15:24.880 --> 00:15:29.839
+if you just say to people which output
+
+00:15:28.600 --> 00:15:31.800
+is better
+
+00:15:29.839 --> 00:15:34.560
+or what is the score of this output
+
+00:15:31.800 --> 00:15:36.360
+people have trouble deciding about that
+
+00:15:34.560 --> 00:15:39.560
+because it's a more subjective
+
+00:15:36.360 --> 00:15:41.680
+evaluation but if I say is this word
+
+00:15:39.560 --> 00:15:43.000
+correct it's a little bit easier for
+
+00:15:41.680 --> 00:15:44.759
+people to do so you can get more
+
+00:15:43.000 --> 00:15:46.920
+consistent annotations
+
+00:15:44.759 --> 00:15:49.720
+here the problem with this is this can
+
+00:15:46.920 --> 00:15:50.839
+be very time consuming so um you know
+
+00:15:49.720 --> 00:15:52.480
+obviously you need to go through and
+
+00:15:50.839 --> 00:15:56.440
+annotate every single error if it's for
+
+00:15:52.480 --> 00:15:56.440
+a long outputs or something your
+
+00:15:56.959 --> 00:16:03.519
+problem so anyway these are just three
+
+00:15:59.800 --> 00:16:05.680
+uh ways of collecting human feedback um
+
+00:16:03.519 --> 00:16:08.639
+and then there's an alternative which is
+
+00:16:05.680 --> 00:16:10.079
+automatic evaluation of outputs and um
+
+00:16:08.639 --> 00:16:14.399
+there's a bunch of different ways we can
+
+00:16:10.079 --> 00:16:16.800
+do this the basic idea here is we have a
+
+00:16:14.399 --> 00:16:20.199
+source um we have a couple
+
+00:16:16.800 --> 00:16:22.800
+hypotheses and uh we have an automatic
+
+00:16:20.199 --> 00:16:26.000
+system that generates outputs uh like
+
+00:16:22.800 --> 00:16:28.279
+scores and we optionally have a
+
+00:16:26.000 --> 00:16:30.839
+reference output so the reference output
+
+00:16:28.279 --> 00:16:33.519
+is a human created gold standard output
+
+00:16:30.839 --> 00:16:35.120
+with respect to how good that um uh with
+
+00:16:33.519 --> 00:16:38.240
+respect to like what the output should
+
+00:16:35.120 --> 00:16:38.240
+be in an ideal
+
+00:16:38.279 --> 00:16:47.079
+case and basically the goal of automatic
+
+00:16:43.199 --> 00:16:50.199
+evaluation is to
+
+00:16:47.079 --> 00:16:52.839
+predict human preferences or to predict
+
+00:16:50.199 --> 00:16:56.240
+what the human scores would be um
+
+00:16:52.839 --> 00:16:58.600
+because still at this point um we mostly
+
+00:16:56.240 --> 00:16:59.480
+view what humans think of the output to
+
+00:16:58.600 --> 00:17:01.680
+be
+
+00:16:59.480 --> 00:17:03.280
+uh kind of the
+
+00:17:01.680 --> 00:17:06.199
+standard
+
+00:17:03.280 --> 00:17:08.439
+and this is called a variety of things
+
+00:17:06.199 --> 00:17:10.600
+depending on what field you're in um in
+
+00:17:08.439 --> 00:17:12.559
+machine translation and summarization
+
+00:17:10.600 --> 00:17:13.520
+it's called automatic evaluation also a
+
+00:17:12.559 --> 00:17:16.520
+lot in
+
+00:17:13.520 --> 00:17:18.400
+dialogue um if you're talking about
+
+00:17:16.520 --> 00:17:21.000
+people from reinforcement learning or
+
+00:17:18.400 --> 00:17:24.600
+other things um or chat Bots or things
+
+00:17:21.000 --> 00:17:28.240
+like that uh a lot of people or uh like
+
+00:17:24.600 --> 00:17:31.280
+AGI or whatever um a lot of people call
+
+00:17:28.240 --> 00:17:32.520
+it uh word model um because that
+
+00:17:31.280 --> 00:17:34.480
+specifically comes from the point of
+
+00:17:32.520 --> 00:17:36.440
+view of like learning from this feedback
+
+00:17:34.480 --> 00:17:37.960
+but essentially they're the same thing
+
+00:17:36.440 --> 00:17:41.080
+uh from my point of view they're trying
+
+00:17:37.960 --> 00:17:42.520
+to predict how good an output is and how
+
+00:17:41.080 --> 00:17:44.240
+much you should reward the model for
+
+00:17:42.520 --> 00:17:46.559
+producing that
+
+00:17:44.240 --> 00:17:48.679
+output
+
+00:17:46.559 --> 00:17:50.520
+um so there's a bunch of different
+
+00:17:48.679 --> 00:17:51.720
+methods to do this I'm not going to
+
+00:17:50.520 --> 00:17:53.799
+cover all of them I'm just going to
+
+00:17:51.720 --> 00:17:55.240
+cover three paradigms for doing this so
+
+00:17:53.799 --> 00:17:57.880
+you know where to look further if you're
+
+00:17:55.240 --> 00:18:00.039
+interested in doing these things um the
+
+00:17:57.880 --> 00:18:02.400
+first one is embedding based
+
+00:18:00.039 --> 00:18:04.679
+evaluation and the way embedding based
+
+00:18:02.400 --> 00:18:06.600
+evaluation works is usually it's
+
+00:18:04.679 --> 00:18:11.400
+unsupervised calculation based on
+
+00:18:06.600 --> 00:18:14.880
+embeding similarity between um
+
+00:18:11.400 --> 00:18:18.080
+the output that the model generated and
+
+00:18:14.880 --> 00:18:20.840
+a reference output that uh you have
+
+00:18:18.080 --> 00:18:23.400
+created so sorry this is very small but
+
+00:18:20.840 --> 00:18:25.559
+we have a reference here that says the
+
+00:18:23.400 --> 00:18:27.640
+weather is cold today and we have a
+
+00:18:25.559 --> 00:18:30.240
+candidate that says it is freezing today
+
+00:18:27.640 --> 00:18:33.000
+so this is probably you know like a good
+
+00:18:30.240 --> 00:18:35.480
+um a reasonably good
+
+00:18:33.000 --> 00:18:37.640
+output and we run this through some
+
+00:18:35.480 --> 00:18:39.120
+embedding model uh it was called Bert
+
+00:18:37.640 --> 00:18:40.679
+score and so of course you can run it
+
+00:18:39.120 --> 00:18:42.240
+through Bert but basically it can be any
+
+00:18:40.679 --> 00:18:43.799
+embedding model that gives you embedding
+
+00:18:42.240 --> 00:18:46.200
+for each token in the
+
+00:18:43.799 --> 00:18:47.640
+sequence and so there are five tokens in
+
+00:18:46.200 --> 00:18:49.720
+this sequence four tokens in this
+
+00:18:47.640 --> 00:18:51.960
+sequence you get five tokens and then
+
+00:18:49.720 --> 00:18:54.799
+four sorry five embeddings and then four
+
+00:18:51.960 --> 00:18:57.400
+embeddings you calculate carewise cosine
+
+00:18:54.799 --> 00:18:59.880
+similarity between all of them and this
+
+00:18:57.400 --> 00:19:03.480
+gives you cosine
+
+00:18:59.880 --> 00:19:06.480
+similarity Matrix and then you take the
+
+00:19:03.480 --> 00:19:09.120
+ARG Max or you take the maximum
+
+00:19:06.480 --> 00:19:11.280
+similarity along either the
+
+00:19:09.120 --> 00:19:15.799
+rows or the
+
+00:19:11.280 --> 00:19:19.559
+columns and here the rows correspond
+
+00:19:15.799 --> 00:19:22.400
+to tokens in the reference and because
+
+00:19:19.559 --> 00:19:24.039
+the rows correspond to tokens in the
+
+00:19:22.400 --> 00:19:26.960
+reference
+
+00:19:24.039 --> 00:19:28.320
+the how well you find something that is
+
+00:19:26.960 --> 00:19:31.679
+similar to each of the tokens in the
+
+00:19:28.320 --> 00:19:34.000
+reference is like a recall based method
+
+00:19:31.679 --> 00:19:35.919
+because it's saying how many tokens in
+
+00:19:34.000 --> 00:19:39.520
+the reference have a good match in the
+
+00:19:35.919 --> 00:19:41.120
+output and then if you look at the
+
+00:19:39.520 --> 00:19:42.799
+columns if you look at the max and the
+
+00:19:41.120 --> 00:19:44.960
+columns this is like a precision based
+
+00:19:42.799 --> 00:19:47.000
+metric because it's saying how many of
+
+00:19:44.960 --> 00:19:49.360
+the things in the output are similar
+
+00:19:47.000 --> 00:19:51.240
+have a similar match in the reference so
+
+00:19:49.360 --> 00:19:54.480
+basically you can calculate recall and
+
+00:19:51.240 --> 00:19:56.200
+precision over all of the tokens and
+
+00:19:54.480 --> 00:20:00.200
+then feed this into something that looks
+
+00:19:56.200 --> 00:20:02.400
+like fmeasure and you can also use tfidf
+
+00:20:00.200 --> 00:20:06.000
+waiting um like what I talked about in
+
+00:20:02.400 --> 00:20:07.799
+the rag lecture uh to upweight low
+
+00:20:06.000 --> 00:20:09.520
+frequency words because low frequency
+
+00:20:07.799 --> 00:20:11.440
+words tend to be more content words and
+
+00:20:09.520 --> 00:20:13.120
+going back to my example you know if you
+
+00:20:11.440 --> 00:20:14.280
+make a mistake from Pittsburgh to Tokyo
+
+00:20:13.120 --> 00:20:17.880
+that's going to be more painful than
+
+00:20:14.280 --> 00:20:21.000
+making a mistake from this to um so
+
+00:20:17.880 --> 00:20:22.520
+actually if you'll uh if you were paying
+
+00:20:21.000 --> 00:20:25.480
+close attention to the rag lecture this
+
+00:20:22.520 --> 00:20:27.360
+looks really similar to the co bear um
+
+00:20:25.480 --> 00:20:29.559
+the co bear retrieval objective that I
+
+00:20:27.360 --> 00:20:30.960
+talked about in the r lecture um I don't
+
+00:20:29.559 --> 00:20:32.840
+think it's a coincidence they both came
+
+00:20:30.960 --> 00:20:34.360
+out around the same time uh so people
+
+00:20:32.840 --> 00:20:36.360
+were thinking about the same thing but
+
+00:20:34.360 --> 00:20:37.600
+um this is one method that's pretty
+
+00:20:36.360 --> 00:20:40.200
+widely
+
+00:20:37.600 --> 00:20:43.480
+use the bird Square code base is also
+
+00:20:40.200 --> 00:20:45.440
+really nice and easy to use so um if uh
+
+00:20:43.480 --> 00:20:47.640
+you want to try it out feel free to take
+
+00:20:45.440 --> 00:20:47.640
+a
+
+00:20:48.159 --> 00:20:53.840
+look cool um the next one I'd like to
+
+00:20:51.600 --> 00:20:56.080
+talk about is a regression based
+
+00:20:53.840 --> 00:20:58.760
+evaluation and the way this works is
+
+00:20:56.080 --> 00:21:02.600
+this is usually used in a supervised uh
+
+00:20:58.760 --> 00:21:04.320
+setting so uh the way what you have to
+
+00:21:02.600 --> 00:21:07.600
+do is you have to calculate a whole
+
+00:21:04.320 --> 00:21:09.799
+bunch of like actual human
+
+00:21:07.600 --> 00:21:12.440
+judgments and
+
+00:21:09.799 --> 00:21:15.000
+usually these judgments can either be
+
+00:21:12.440 --> 00:21:16.960
+direct assessment uh where you actually
+
+00:21:15.000 --> 00:21:19.120
+have a score or they can be pairwise
+
+00:21:16.960 --> 00:21:20.840
+judgments and then if you have direct
+
+00:21:19.120 --> 00:21:23.640
+assessment you use a regression based
+
+00:21:20.840 --> 00:21:26.039
+loss like uh minimum squared error if
+
+00:21:23.640 --> 00:21:27.520
+you have pairwise uh you use a ranking
+
+00:21:26.039 --> 00:21:29.039
+based loss that tries to upweight the
+
+00:21:27.520 --> 00:21:31.360
+ones that are higher scoring downward
+
+00:21:29.039 --> 00:21:33.200
+the ones that are lower scoring one
+
+00:21:31.360 --> 00:21:35.720
+typical example of this is Comet which
+
+00:21:33.200 --> 00:21:37.200
+is or has been at least for a very long
+
+00:21:35.720 --> 00:21:39.880
+time the state-of-the art and machine
+
+00:21:37.200 --> 00:21:41.279
+translation evaluation and the reason
+
+00:21:39.880 --> 00:21:43.440
+why it works so well is because we have
+
+00:21:41.279 --> 00:21:44.720
+a bunch of evaluations for machine
+
+00:21:43.440 --> 00:21:46.080
+translation they've been doing
+
+00:21:44.720 --> 00:21:47.600
+evaluation and machine translation
+
+00:21:46.080 --> 00:21:50.480
+systems for years and you can use that
+
+00:21:47.600 --> 00:21:52.720
+as lots of supervised training data so
+
+00:21:50.480 --> 00:21:54.640
+basically you just take um these
+
+00:21:52.720 --> 00:21:56.440
+evaluation data you have human
+
+00:21:54.640 --> 00:21:59.080
+annotations you have the output
+
+00:21:56.440 --> 00:22:00.320
+according to a model like comet um you
+
+00:21:59.080 --> 00:22:02.679
+calculate the difference between them
+
+00:22:00.320 --> 00:22:05.640
+and you update model
+
+00:22:02.679 --> 00:22:07.080
+parameters um the problem this is great
+
+00:22:05.640 --> 00:22:08.520
+if you have lots of training data the
+
+00:22:07.080 --> 00:22:10.640
+problem with this is for a lot of tasks
+
+00:22:08.520 --> 00:22:12.360
+we don't have lots of training data so
+
+00:22:10.640 --> 00:22:14.720
+um you know training these is a little
+
+00:22:12.360 --> 00:22:14.720
+bit less
+
+00:22:15.400 --> 00:22:22.919
+feasible and now recently uh what we
+
+00:22:19.600 --> 00:22:25.279
+have been moving into is is a QA based
+
+00:22:22.919 --> 00:22:27.120
+evaluation which is basically where we
+
+00:22:25.279 --> 00:22:30.760
+ask a language model how good the output
+
+00:22:27.120 --> 00:22:32.279
+is and so uh gmba is an example one of
+
+00:22:30.760 --> 00:22:34.559
+the early examples of this for machine
+
+00:22:32.279 --> 00:22:37.320
+translation evaluation uh where they
+
+00:22:34.559 --> 00:22:39.840
+basically just ask a g gp4 like score
+
+00:22:37.320 --> 00:22:41.600
+the following translation from Source
+
+00:22:39.840 --> 00:22:44.000
+language to target language with respect
+
+00:22:41.600 --> 00:22:47.080
+to the human reference um on a
+
+00:22:44.000 --> 00:22:49.200
+continuous scale from Z to 100 uh where
+
+00:22:47.080 --> 00:22:51.320
+the score of zero means no meaning
+
+00:22:49.200 --> 00:22:54.039
+preserved and the score of 100 means a
+
+00:22:51.320 --> 00:22:56.880
+perfect meaning in grammar uh you feed
+
+00:22:54.039 --> 00:22:58.760
+in the source um you feed in the T the
+
+00:22:56.880 --> 00:23:01.000
+human reference optionally if you have a
+
+00:22:58.760 --> 00:23:03.320
+human reference and then you feed in the
+
+00:23:01.000 --> 00:23:06.760
+Target um and you get a
+
+00:23:03.320 --> 00:23:09.919
+score and um so this this works pretty
+
+00:23:06.760 --> 00:23:12.720
+well this can give you uh better results
+
+00:23:09.919 --> 00:23:15.159
+um there's a especially if you have a
+
+00:23:12.720 --> 00:23:16.960
+strong language model the problem is
+
+00:23:15.159 --> 00:23:18.279
+it's very unpredictable whether this is
+
+00:23:16.960 --> 00:23:20.120
+going to work well and it's very
+
+00:23:18.279 --> 00:23:23.039
+dependent on the prompt that you're
+
+00:23:20.120 --> 00:23:25.279
+using so um right now A lot of people
+
+00:23:23.039 --> 00:23:27.279
+are using gp4 without actually
+
+00:23:25.279 --> 00:23:29.039
+validating whether it does a good job at
+
+00:23:27.279 --> 00:23:33.080
+evaluation and
+
+00:23:29.039 --> 00:23:34.919
+and my the results are all across the
+
+00:23:33.080 --> 00:23:36.880
+board it can be anywhere from very very
+
+00:23:34.919 --> 00:23:38.640
+good to very very bad at evaluating
+
+00:23:36.880 --> 00:23:41.320
+particular tasks so I would be at least
+
+00:23:38.640 --> 00:23:43.559
+a little bit suspicious of whether gp4
+
+00:23:41.320 --> 00:23:45.679
+is doing a good job evaluating for your
+
+00:23:43.559 --> 00:23:49.320
+task especially more complex
+
+00:23:45.679 --> 00:23:51.960
+tests um I would especially be
+
+00:23:49.320 --> 00:23:54.000
+suspicious if you're doing two uh any of
+
+00:23:51.960 --> 00:23:56.760
+the two following things number one if
+
+00:23:54.000 --> 00:23:59.880
+you're comparing gp4 or any model
+
+00:23:56.760 --> 00:24:02.400
+against itself in another model because
+
+00:23:59.880 --> 00:24:05.200
+gp4 really likes
+
+00:24:02.400 --> 00:24:06.880
+gp4 it really likes its own outputs and
+
+00:24:05.200 --> 00:24:08.120
+there are papers uh sorry I don't
+
+00:24:06.880 --> 00:24:09.679
+actually have the references here but I
+
+00:24:08.120 --> 00:24:11.200
+can follow up if people are interested
+
+00:24:09.679 --> 00:24:13.080
+but there are papers that demonstrate
+
+00:24:11.200 --> 00:24:15.799
+that gp4 likes it you know its own
+
+00:24:13.080 --> 00:24:19.200
+outputs more than others also if you're
+
+00:24:15.799 --> 00:24:22.120
+explicitly optimizing the outputs using
+
+00:24:19.200 --> 00:24:24.640
+rlf um there is something called good
+
+00:24:22.120 --> 00:24:27.120
+Hearts law which is basically anytime
+
+00:24:24.640 --> 00:24:29.520
+you uh start optimizing towards a metric
+
+00:24:27.120 --> 00:24:32.559
+it becomes a bad metric and that also
+
+00:24:29.520 --> 00:24:35.000
+happens for gp4 based evaluations so if
+
+00:24:32.559 --> 00:24:37.200
+you start optimizing for gp4 based
+
+00:24:35.000 --> 00:24:38.960
+evaluations especially for reference
+
+00:24:37.200 --> 00:24:41.679
+list metrics that don't use a reference
+
+00:24:38.960 --> 00:24:44.840
+output then um you start basically
+
+00:24:41.679 --> 00:24:47.440
+exploiting the metric
+
+00:24:44.840 --> 00:24:49.840
+um another thing that you can do with QA
+
+00:24:47.440 --> 00:24:53.279
+based evaluation is ask about fine grade
+
+00:24:49.840 --> 00:24:54.919
+mistakes and so this is a paper by um uh
+
+00:24:53.279 --> 00:24:56.480
+Patrick Fernandez who's a student who's
+
+00:24:54.919 --> 00:25:02.080
+working with me and basically what we
+
+00:24:56.480 --> 00:25:05.240
+did is we asked the model to um not give
+
+00:25:02.080 --> 00:25:07.360
+a particular score but actually identify
+
+00:25:05.240 --> 00:25:08.880
+the mistakes in the output and when we
+
+00:25:07.360 --> 00:25:10.559
+asked it to identify the mistakes in the
+
+00:25:08.880 --> 00:25:13.720
+output we found that this gave more
+
+00:25:10.559 --> 00:25:17.320
+consistent uh results so kind of
+
+00:25:13.720 --> 00:25:18.840
+interestingly we ask humans to identify
+
+00:25:17.320 --> 00:25:21.120
+individual mistakes and the output that
+
+00:25:18.840 --> 00:25:24.240
+gives humans more consistent results
+
+00:25:21.120 --> 00:25:25.559
+it's the same thing for gp4 so um that
+
+00:25:24.240 --> 00:25:27.320
+that's another paper you can look at if
+
+00:25:25.559 --> 00:25:29.640
+you're
+
+00:25:27.320 --> 00:25:32.679
+interested
+
+00:25:29.640 --> 00:25:38.000
+cool um so I I mentioned that you could
+
+00:25:32.679 --> 00:25:38.000
+or could not uh trust uh yeah sorry go
+
+00:25:44.679 --> 00:25:51.279
+ahead uh correct so yeah B basically
+
+00:25:47.360 --> 00:25:53.279
+just what you do is you have the source
+
+00:25:51.279 --> 00:25:54.960
+um ideally you'll also have a reference
+
+00:25:53.279 --> 00:25:57.840
+output that was created by skilled
+
+00:25:54.960 --> 00:25:59.720
+humans and then you put in the Target
+
+00:25:57.840 --> 00:26:02.279
+you know output basically you have the
+
+00:25:59.720 --> 00:26:08.000
+input ideally a reference output created
+
+00:26:02.279 --> 00:26:08.000
+by Good by skilled humans and uh like
+
+00:26:15.159 --> 00:26:20.240
+hypothesis yeah I
+
+00:26:17.919 --> 00:26:24.559
+mean it's a good question and I don't
+
+00:26:20.240 --> 00:26:26.919
+know if we actually have a a very clear
+
+00:26:24.559 --> 00:26:31.399
+empirical like evidence of why this is
+
+00:26:26.919 --> 00:26:33.320
+the case but my hypothesis about this is
+
+00:26:31.399 --> 00:26:36.159
+yes we kind of would expect models to be
+
+00:26:33.320 --> 00:26:38.200
+more biased towards their own outputs
+
+00:26:36.159 --> 00:26:40.919
+and the reason why is because
+
+00:26:38.200 --> 00:26:43.080
+essentially you know models
+
+00:26:40.919 --> 00:26:44.279
+are within their embeddings they're
+
+00:26:43.080 --> 00:26:45.760
+encoding when they're in a high
+
+00:26:44.279 --> 00:26:47.600
+probability part of the space and when
+
+00:26:45.760 --> 00:26:50.200
+they're in a low probability part of the
+
+00:26:47.600 --> 00:26:51.120
+space and like the high probability part
+
+00:26:50.200 --> 00:26:54.600
+of the
+
+00:26:51.120 --> 00:26:56.200
+space is going to be the high
+
+00:26:54.600 --> 00:26:58.600
+probability part of the space is going
+
+00:26:56.200 --> 00:27:02.559
+to be associated with good outputs
+
+00:26:58.600 --> 00:27:07.000
+because like when
+
+00:27:02.559 --> 00:27:08.600
+models are more sure of their outputs
+
+00:27:07.000 --> 00:27:11.960
+they're more likely to be
+
+00:27:08.600 --> 00:27:13.520
+good just because that indicates that
+
+00:27:11.960 --> 00:27:15.240
+like they're closer to the training data
+
+00:27:13.520 --> 00:27:17.760
+that it had and other things like that
+
+00:27:15.240 --> 00:27:21.600
+so model probabilities are associated
+
+00:27:17.760 --> 00:27:23.760
+with outputs uh with uh with good
+
+00:27:21.600 --> 00:27:26.600
+outputs but just
+
+00:27:23.760 --> 00:27:29.440
+correla separately from
+
+00:27:26.600 --> 00:27:32.120
+that I believe a model can identify when
+
+00:27:29.440 --> 00:27:33.320
+it's in a high probability segment of
+
+00:27:32.120 --> 00:27:35.799
+the space and when it's in a low
+
+00:27:33.320 --> 00:27:39.399
+probability segment of the space and
+
+00:27:35.799 --> 00:27:39.399
+because of that I expect
+
+00:27:39.519 --> 00:27:45.519
+that I like there are segments of the
+
+00:27:43.240 --> 00:27:47.120
+embedding space where it's more likely
+
+00:27:45.519 --> 00:27:48.360
+to answer yes about something being good
+
+00:27:47.120 --> 00:27:50.960
+or not and those are going to be
+
+00:27:48.360 --> 00:27:54.760
+associated with high uh like high
+
+00:27:50.960 --> 00:27:56.159
+probability outbreaks as well and also
+
+00:27:54.760 --> 00:27:57.760
+models are more likely to generate
+
+00:27:56.159 --> 00:28:00.240
+outputs that are high probability
+
+00:27:57.760 --> 00:28:02.320
+according into their model by definition
+
+00:28:00.240 --> 00:28:03.880
+so all three of those effects together
+
+00:28:02.320 --> 00:28:05.640
+would basically go into a model being
+
+00:28:03.880 --> 00:28:09.120
+bios supports its own outputs compared
+
+00:28:05.640 --> 00:28:11.559
+to that puts in another model but um
+
+00:28:09.120 --> 00:28:13.279
+yeah this is a very handwavy explanation
+
+00:28:11.559 --> 00:28:15.519
+but like putting the two the three
+
+00:28:13.279 --> 00:28:18.600
+together models output high probability
+
+00:28:15.519 --> 00:28:20.880
+things from their own probability Space
+
+00:28:18.600 --> 00:28:23.440
+by definition
+
+00:28:20.880 --> 00:28:25.760
+um things that are high probability are
+
+00:28:23.440 --> 00:28:27.519
+associated with being good uh just
+
+00:28:25.760 --> 00:28:29.279
+because otherwise a model would be
+
+00:28:27.519 --> 00:28:31.840
+outputting garbage
+
+00:28:29.279 --> 00:28:33.840
+and um the final thing which is more
+
+00:28:31.840 --> 00:28:35.679
+tenuous is if the model is in a high
+
+00:28:33.840 --> 00:28:37.919
+probability segment of the space it's
+
+00:28:35.679 --> 00:28:39.760
+more likely to Output yes according to a
+
+00:28:37.919 --> 00:28:41.480
+question of it being good and I I think
+
+00:28:39.760 --> 00:28:44.360
+that's probably true but I'm not 100%
+
+00:28:41.480 --> 00:28:44.360
+sure about the the
+
+00:28:45.559 --> 00:28:51.039
+fin um maybe maybe someone wants to
+
+00:28:49.000 --> 00:28:52.840
+examinate examine that as a final
+
+00:28:51.039 --> 00:28:54.200
+project it seems like a interesting
+
+00:28:52.840 --> 00:28:57.080
+interesting
+
+00:28:54.200 --> 00:29:00.039
+question um cool uh were there any other
+
+00:28:57.080 --> 00:29:00.039
+questions about these methods
+
+00:29:00.159 --> 00:29:07.120
+here um okay so when I say like an
+
+00:29:03.960 --> 00:29:11.080
+evaluation metric is good or not what do
+
+00:29:07.120 --> 00:29:13.200
+I mean by this being good or not um or a
+
+00:29:11.080 --> 00:29:16.880
+reward model or whatever else and
+
+00:29:13.200 --> 00:29:18.440
+basically the um the way we typically do
+
+00:29:16.880 --> 00:29:19.840
+this is by doing something called meta
+
+00:29:18.440 --> 00:29:22.440
+evaluation so it's called meta
+
+00:29:19.840 --> 00:29:25.799
+evaluation because it's evaluation of
+
+00:29:22.440 --> 00:29:29.279
+evaluation and uh the way we do this is
+
+00:29:25.799 --> 00:29:32.519
+we have human uh scores and we have
+
+00:29:29.279 --> 00:29:34.760
+automatic scores and we usually
+
+00:29:32.519 --> 00:29:38.640
+calculate some sort of correlation
+
+00:29:34.760 --> 00:29:41.000
+between the scores so um typical ones
+
+00:29:38.640 --> 00:29:46.440
+are rank correlations like Pearson's
+
+00:29:41.000 --> 00:29:48.799
+correlation or tendle uh Tow and uh so
+
+00:29:46.440 --> 00:29:51.200
+the more Associated the automatic scores
+
+00:29:48.799 --> 00:29:53.960
+are with the human scores the higher
+
+00:29:51.200 --> 00:29:55.159
+these correlations are going to be um
+
+00:29:53.960 --> 00:29:57.559
+there's other things that you can
+
+00:29:55.159 --> 00:30:00.080
+calculate so if you're trying to figure
+
+00:29:57.559 --> 00:30:01.640
+out whether a model um matches human
+
+00:30:00.080 --> 00:30:04.279
+pairwise preferences you can just
+
+00:30:01.640 --> 00:30:06.440
+calculate accuracy so I didn't put that
+
+00:30:04.279 --> 00:30:08.080
+on um I didn't put that on the slide
+
+00:30:06.440 --> 00:30:10.880
+here but you can just calculate accuracy
+
+00:30:08.080 --> 00:30:13.120
+of pairwise preferences um you can also
+
+00:30:10.880 --> 00:30:15.360
+calculate the absolute error between the
+
+00:30:13.120 --> 00:30:19.320
+the judgments if you want to know uh
+
+00:30:15.360 --> 00:30:21.720
+whether the absolute error matches so um
+
+00:30:19.320 --> 00:30:24.159
+the these are good things to do if you
+
+00:30:21.720 --> 00:30:25.600
+want to use an evaluation metric but you
+
+00:30:24.159 --> 00:30:27.200
+aren't sure whether it's good or not I
+
+00:30:25.600 --> 00:30:29.640
+would check to see whether the authors
+
+00:30:27.200 --> 00:30:32.000
+have done this sort of meta evaluation
+
+00:30:29.640 --> 00:30:33.760
+if they haven't be a little bit
+
+00:30:32.000 --> 00:30:36.960
+suspicious if they have be a little bit
+
+00:30:33.760 --> 00:30:39.799
+less suspicious but um
+
+00:30:36.960 --> 00:30:42.960
+yeah how do people do this typically uh
+
+00:30:39.799 --> 00:30:45.640
+usually they create uh data sets like
+
+00:30:42.960 --> 00:30:49.440
+the WM they use data sets like the WMT
+
+00:30:45.640 --> 00:30:53.960
+shared tasks um or
+
+00:30:49.440 --> 00:30:57.679
+uh uh like some evl um but there's also
+
+00:30:53.960 --> 00:30:59.960
+other ways to create um uh there's also
+
+00:30:57.679 --> 00:31:01.639
+Lots other data sets but in order to do
+
+00:30:59.960 --> 00:31:05.639
+this reliably you need a fairly large
+
+00:31:01.639 --> 00:31:05.639
+data set so it's one thing to be aware
+
+00:31:07.080 --> 00:31:10.760
+of
+
+00:31:08.720 --> 00:31:14.200
+cool
+
+00:31:10.760 --> 00:31:16.360
+um then the final thing um all of the
+
+00:31:14.200 --> 00:31:17.919
+automatic evaluation methods that I
+
+00:31:16.360 --> 00:31:20.240
+talked about now are trying to match
+
+00:31:17.919 --> 00:31:22.679
+human preferences but that's not the
+
+00:31:20.240 --> 00:31:24.960
+only thing that you necessarily want to
+
+00:31:22.679 --> 00:31:28.440
+do the final thing that you might want
+
+00:31:24.960 --> 00:31:30.840
+to do is uh use the model outputs in a
+
+00:31:28.440 --> 00:31:34.200
+downstream system and see whether they
+
+00:31:30.840 --> 00:31:36.399
+are effective for that so there's two
+
+00:31:34.200 --> 00:31:39.080
+concepts of intrinsic evaluation and
+
+00:31:36.399 --> 00:31:41.720
+extrinsic evaluation so intrinsic
+
+00:31:39.080 --> 00:31:44.159
+evaluation um evaluates the quality of
+
+00:31:41.720 --> 00:31:45.720
+the output itself and so that would be
+
+00:31:44.159 --> 00:31:48.639
+like asking a human directly about how
+
+00:31:45.720 --> 00:31:50.720
+good is this output extrinsic evaluation
+
+00:31:48.639 --> 00:31:53.679
+is evaluating output quality by its
+
+00:31:50.720 --> 00:31:57.000
+utility um and so just to give one
+
+00:31:53.679 --> 00:31:58.360
+example um if you can evaluate large
+
+00:31:57.000 --> 00:32:00.200
+language model summary
+
+00:31:58.360 --> 00:32:04.200
+through question answering
+
+00:32:00.200 --> 00:32:05.880
+accuracy um and so you can take the
+
+00:32:04.200 --> 00:32:07.399
+output of an llm and feed it through a
+
+00:32:05.880 --> 00:32:09.600
+question answering model and see whether
+
+00:32:07.399 --> 00:32:12.399
+you're able to answer questions based on
+
+00:32:09.600 --> 00:32:15.799
+this and that kind of gives you a better
+
+00:32:12.399 --> 00:32:18.279
+idea of whether the summary require uh
+
+00:32:15.799 --> 00:32:20.120
+incorporates requisite information but
+
+00:32:18.279 --> 00:32:22.120
+if you think about anything an llm can
+
+00:32:20.120 --> 00:32:23.760
+be used for usually it's part of a
+
+00:32:22.120 --> 00:32:26.679
+bigger system so you can evaluate it as
+
+00:32:23.760 --> 00:32:28.399
+a part of that bigger system um the
+
+00:32:26.679 --> 00:32:30.639
+problem with this is it's a very
+
+00:32:28.399 --> 00:32:33.960
+indirect way of assessing things so like
+
+00:32:30.639 --> 00:32:36.080
+let's say your QA model is just bad uh
+
+00:32:33.960 --> 00:32:38.480
+how can you disentangle the effect of
+
+00:32:36.080 --> 00:32:41.679
+the L summary versus the QA model that's
+
+00:32:38.480 --> 00:32:44.120
+not a trivial thing to do so ideally
+
+00:32:41.679 --> 00:32:47.000
+like a combination of these two is
+
+00:32:44.120 --> 00:32:47.000
+practically the best way
+
+00:32:48.039 --> 00:32:52.200
+go cool so
+
+00:32:56.039 --> 00:32:59.960
+yeah yeah it wouldn't necessar
+
+00:32:58.360 --> 00:33:05.679
+say it's harder to do it might even be
+
+00:32:59.960 --> 00:33:05.679
+easier to do um which is like let's
+
+00:33:06.679 --> 00:33:11.720
+say Let me let me see if I can come up
+
+00:33:09.360 --> 00:33:11.720
+with
+
+00:33:12.639 --> 00:33:17.600
+example what let's
+
+00:33:15.000 --> 00:33:19.670
+say you
+
+00:33:17.600 --> 00:33:22.979
+are trying
+
+00:33:19.670 --> 00:33:22.979
+[Music]
+
+00:33:24.639 --> 00:33:29.760
+to let's say you're trying to
+
+00:33:30.559 --> 00:33:33.559
+guess
+
+00:33:39.000 --> 00:33:45.399
+whether let's say you're trying to guess
+
+00:33:42.399 --> 00:33:46.559
+whether a someone will be hired at a
+
+00:33:45.399 --> 00:33:52.039
+company or
+
+00:33:46.559 --> 00:33:53.880
+not based on an llm generated summary of
+
+00:33:52.039 --> 00:33:58.880
+their qualifications for a position or
+
+00:33:53.880 --> 00:34:01.799
+something like that um and
+
+00:33:58.880 --> 00:34:03.080
+you what actually maybe this is not a
+
+00:34:01.799 --> 00:34:04.720
+great example because whether you should
+
+00:34:03.080 --> 00:34:06.960
+be doing this ethically is a little bit
+
+00:34:04.720 --> 00:34:08.159
+unclear but let's say you were doing
+
+00:34:06.960 --> 00:34:09.560
+let's say you were doing something like
+
+00:34:08.159 --> 00:34:11.520
+that just because it's one example I can
+
+00:34:09.560 --> 00:34:14.320
+think of right now whether they will get
+
+00:34:11.520 --> 00:34:16.320
+hired or not is um is clear because you
+
+00:34:14.320 --> 00:34:19.399
+have a objective answer right whether
+
+00:34:16.320 --> 00:34:21.480
+they were hired or not um or maybe maybe
+
+00:34:19.399 --> 00:34:23.800
+another example would be like let's say
+
+00:34:21.480 --> 00:34:26.320
+um let's say you want to predict the
+
+00:34:23.800 --> 00:34:29.599
+diagnosis in a medical application based
+
+00:34:26.320 --> 00:34:32.960
+on an llm generated some of somebody's
+
+00:34:29.599 --> 00:34:35.919
+uh you know LM generated summary of
+
+00:34:32.960 --> 00:34:38.480
+somebody's you know past medical history
+
+00:34:35.919 --> 00:34:40.839
+and all this stuff and here you want the
+
+00:34:38.480 --> 00:34:43.440
+llm generated summary you definitely
+
+00:34:40.839 --> 00:34:44.879
+want the summary because the summary is
+
+00:34:43.440 --> 00:34:47.560
+going to be viewed by a doctor who will
+
+00:34:44.879 --> 00:34:49.359
+make the final decision but you also
+
+00:34:47.560 --> 00:34:50.760
+have information about the diagnoses of
+
+00:34:49.359 --> 00:34:52.399
+all the people in your medical system
+
+00:34:50.760 --> 00:34:54.560
+later because you know they went through
+
+00:34:52.399 --> 00:34:56.480
+your medical system for years and you
+
+00:34:54.560 --> 00:34:58.200
+know later like through lots of tests
+
+00:34:56.480 --> 00:35:00.800
+and stuff uh whether how they were
+
+00:34:58.200 --> 00:35:02.320
+diagnosed so you generate an LM based
+
+00:35:00.800 --> 00:35:05.000
+summary and then you predict the
+
+00:35:02.320 --> 00:35:06.599
+diagnosis from the summary so there the
+
+00:35:05.000 --> 00:35:08.040
+evaluation of the diagnosis is very
+
+00:35:06.599 --> 00:35:11.480
+clear because you kind of have a gold
+
+00:35:08.040 --> 00:35:12.599
+standard answer um but the EV intrinsic
+
+00:35:11.480 --> 00:35:14.839
+evaluation of whether it's a good
+
+00:35:12.599 --> 00:35:16.839
+summary or not is not as clear because
+
+00:35:14.839 --> 00:35:19.400
+you'd have pass do whether it's good and
+
+00:35:16.839 --> 00:35:21.079
+understandable summary so the extrinsic
+
+00:35:19.400 --> 00:35:24.920
+evaluation might be easier because it's
+
+00:35:21.079 --> 00:35:26.480
+clearer um so there are cases like that
+
+00:35:24.920 --> 00:35:30.720
+um the problem is you would have to have
+
+00:35:26.480 --> 00:35:33.800
+that data in order to do that um yeah do
+
+00:35:30.720 --> 00:35:38.240
+like evaluation yeah I was just
+
+00:35:33.800 --> 00:35:40.800
+wondering typically the
+
+00:35:38.240 --> 00:35:42.880
+like like how do you accomodate the
+
+00:35:40.800 --> 00:35:47.160
+diversity oh yeah that's a great that's
+
+00:35:42.880 --> 00:35:50.240
+a great question um so how do you how do
+
+00:35:47.160 --> 00:35:50.240
+you get these scores
+
+00:35:50.720 --> 00:35:55.800
+here there's a number of different
+
+00:35:53.200 --> 00:35:59.160
+things in the WMT shared tasks what they
+
+00:35:55.800 --> 00:36:00.280
+did is they did
+
+00:35:59.160 --> 00:36:03.200
+the first thing they do is they
+
+00:36:00.280 --> 00:36:06.319
+normalize by annotator and what they do
+
+00:36:03.200 --> 00:36:10.400
+is they basically take the zcore or Z
+
+00:36:06.319 --> 00:36:12.240
+score of the um of the human annotator's
+
+00:36:10.400 --> 00:36:14.880
+actual scores because some people are
+
+00:36:12.240 --> 00:36:16.400
+more harsh than other people and so what
+
+00:36:14.880 --> 00:36:20.680
+that means is you basically normalize to
+
+00:36:16.400 --> 00:36:22.119
+have zero mean in unit variance um and
+
+00:36:20.680 --> 00:36:24.119
+then after they've normalized to zero
+
+00:36:22.119 --> 00:36:29.560
+mean and unit variance then I think they
+
+00:36:24.119 --> 00:36:29.560
+average together different humans so um
+
+00:36:30.160 --> 00:36:36.520
+then for how do you deal with the fact
+
+00:36:33.680 --> 00:36:38.040
+that humans disagree on things and I
+
+00:36:36.520 --> 00:36:39.480
+think it's pretty varied I don't know if
+
+00:36:38.040 --> 00:36:42.160
+there's any gold standard way of doing
+
+00:36:39.480 --> 00:36:43.839
+it but sometimes you just average
+
+00:36:42.160 --> 00:36:46.359
+sometimes you throw away examples where
+
+00:36:43.839 --> 00:36:47.960
+humans disagree a lot um because like
+
+00:36:46.359 --> 00:36:50.200
+you can't get the humans to agree how
+
+00:36:47.960 --> 00:36:53.319
+could you expect how could you expect a
+
+00:36:50.200 --> 00:36:55.119
+machine to do well um so I think it it's
+
+00:36:53.319 --> 00:36:59.200
+a little bit test
+
+00:36:55.119 --> 00:37:01.560
+defending yeah so for
+
+00:36:59.200 --> 00:37:04.560
+generation inin
+
+00:37:01.560 --> 00:37:06.280
+andin yeah so for code generation that's
+
+00:37:04.560 --> 00:37:08.200
+I I I love this example because I've
+
+00:37:06.280 --> 00:37:09.960
+worked on code generation a lot of
+
+00:37:08.200 --> 00:37:12.680
+people only think about extrinsic
+
+00:37:09.960 --> 00:37:14.400
+evaluation of code Generation Um or I
+
+00:37:12.680 --> 00:37:16.160
+don't know if it's extrinsic but only
+
+00:37:14.400 --> 00:37:19.160
+think about execution based evaluation
+
+00:37:16.160 --> 00:37:20.520
+of code generation which is like you
+
+00:37:19.160 --> 00:37:22.400
+execute the code you see whether it
+
+00:37:20.520 --> 00:37:25.040
+passs unit tests and other things like
+
+00:37:22.400 --> 00:37:26.839
+this but in reality actually there's a
+
+00:37:25.040 --> 00:37:28.599
+lot of other important things for code
+
+00:37:26.839 --> 00:37:30.560
+like readability and other stuff like
+
+00:37:28.599 --> 00:37:32.160
+that and you should be evaluating those
+
+00:37:30.560 --> 00:37:34.920
+things but I think a lot of people like
+
+00:37:32.160 --> 00:37:36.520
+kind of ignore that so um there there
+
+00:37:34.920 --> 00:37:38.880
+are a few Pap that do that but most of
+
+00:37:36.520 --> 00:37:41.000
+the time people just execute the Cod
+
+00:37:38.880 --> 00:37:45.520
+process
+
+00:37:41.000 --> 00:37:47.760
+un cool okay um so yeah moving on to the
+
+00:37:45.520 --> 00:37:51.160
+learning part so now I'd like to talk
+
+00:37:47.760 --> 00:37:55.280
+about uh learning and the first thing
+
+00:37:51.160 --> 00:37:59.480
+I'll cover is error and risk and so
+
+00:37:55.280 --> 00:38:02.280
+basically um the way we calculate air is
+
+00:37:59.480 --> 00:38:03.119
+we generate an output and we calculate
+
+00:38:02.280 --> 00:38:07.680
+its
+
+00:38:03.119 --> 00:38:09.480
+Badness um and so generating the output
+
+00:38:07.680 --> 00:38:13.160
+could be argmax it could be sampling it
+
+00:38:09.480 --> 00:38:15.800
+could be anything else like that um and
+
+00:38:13.160 --> 00:38:18.640
+we calculate its Badness uh which is one
+
+00:38:15.800 --> 00:38:21.040
+minus in which could be like how bad is
+
+00:38:18.640 --> 00:38:22.720
+the output uh if you're you have a
+
+00:38:21.040 --> 00:38:24.760
+Badness measure or it could be one minus
+
+00:38:22.720 --> 00:38:28.400
+the evaluation Square to calculate its
+
+00:38:24.760 --> 00:38:30.160
+Badness and this is defined as error
+
+00:38:28.400 --> 00:38:31.440
+and generally what you want to do is you
+
+00:38:30.160 --> 00:38:33.520
+want to minimize
+
+00:38:31.440 --> 00:38:36.800
+error
+
+00:38:33.520 --> 00:38:39.400
+um because in the end you're going to be
+
+00:38:36.800 --> 00:38:42.359
+deploying A system that just outputs you
+
+00:38:39.400 --> 00:38:46.079
+know one thing and uh you're going to
+
+00:38:42.359 --> 00:38:49.800
+want that to be as good a thing as
+
+00:38:46.079 --> 00:38:53.000
+possible um but the problem with this is
+
+00:38:49.800 --> 00:38:56.400
+there's no easy way to actually optimize
+
+00:38:53.000 --> 00:38:59.079
+this value in especially in a text
+
+00:38:56.400 --> 00:39:01.800
+generation sty setting but even in the
+
+00:38:59.079 --> 00:39:06.839
+classification setting we can't easily
+
+00:39:01.800 --> 00:39:06.839
+maximize err because um if you look at
+
+00:39:09.040 --> 00:39:14.200
+the if you look at the surface of air uh
+
+00:39:12.760 --> 00:39:15.960
+at some point you're going to have a
+
+00:39:14.200 --> 00:39:18.319
+non-differentiable part when you take
+
+00:39:15.960 --> 00:39:21.119
+the argmax and or when you do sampling
+
+00:39:18.319 --> 00:39:23.319
+or anything like that so um you're not
+
+00:39:21.119 --> 00:39:27.119
+going to be able to do gradient based
+
+00:39:23.319 --> 00:39:29.200
+optimization so what we do normally is
+
+00:39:27.119 --> 00:39:33.400
+um
+
+00:39:29.200 --> 00:39:37.000
+we instead calculate something uh called
+
+00:39:33.400 --> 00:39:38.560
+risk and what risk looks like is uh we
+
+00:39:37.000 --> 00:39:40.599
+talked a little bit about minimum based
+
+00:39:38.560 --> 00:39:43.520
+risk for decoding but this is for uh
+
+00:39:40.599 --> 00:39:46.160
+training time and what it looks like is
+
+00:39:43.520 --> 00:39:49.040
+it's essentially the expected err of the
+
+00:39:46.160 --> 00:39:52.359
+output and the expected err of the
+
+00:39:49.040 --> 00:39:54.760
+output um includes a probability in the
+
+00:39:52.359 --> 00:39:58.240
+objective function here and that
+
+00:39:54.760 --> 00:40:01.079
+probability uh is differential basically
+
+00:39:58.240 --> 00:40:02.319
+so we can um uh we can easily do
+
+00:40:01.079 --> 00:40:05.720
+gradient based
+
+00:40:02.319 --> 00:40:09.119
+optimization through it um the problem
+
+00:40:05.720 --> 00:40:12.200
+with this is It's differentiable but for
+
+00:40:09.119 --> 00:40:17.160
+text generation for example the sum is
+
+00:40:12.200 --> 00:40:20.319
+intractable because we have a combinator
+
+00:40:17.160 --> 00:40:23.880
+large number of potential outputs um
+
+00:40:20.319 --> 00:40:25.520
+because you know if this is we've talked
+
+00:40:23.880 --> 00:40:28.720
+about this before but if this is like
+
+00:40:25.520 --> 00:40:30.680
+link you know 50 and we have a 30,000
+
+00:40:28.720 --> 00:40:32.839
+vocabul that's 30,000 to the 50
+
+00:40:30.680 --> 00:40:34.599
+possibilities we can't take a su over
+
+00:40:32.839 --> 00:40:36.359
+that many
+
+00:40:34.599 --> 00:40:38.400
+possibilities
+
+00:40:36.359 --> 00:40:42.680
+um
+
+00:40:38.400 --> 00:40:45.839
+so minimum R risk training uh tries to
+
+00:40:42.680 --> 00:40:48.440
+minimize risk reinforcement learning
+
+00:40:45.839 --> 00:40:50.040
+also many of the models especially
+
+00:40:48.440 --> 00:40:53.599
+policy gradient models are trying to
+
+00:40:50.040 --> 00:40:55.240
+minimize risk as well so um but the
+
+00:40:53.599 --> 00:40:58.040
+reason why I wanted to talk about risk
+
+00:40:55.240 --> 00:41:00.440
+first is because this is very simple to
+
+00:40:58.040 --> 00:41:01.640
+get to from the uh the point of view of
+
+00:41:00.440 --> 00:41:06.560
+like all the things that we've studied
+
+00:41:01.640 --> 00:41:06.560
+so so I think it's talking about
+
+00:41:06.760 --> 00:41:11.800
+that
+
+00:41:08.319 --> 00:41:15.520
+um one other thing that I should mention
+
+00:41:11.800 --> 00:41:18.400
+about is
+
+00:41:15.520 --> 00:41:23.079
+um or no sorry I'll I'll talk about that
+
+00:41:18.400 --> 00:41:26.880
+later so when we want to optimize risk
+
+00:41:23.079 --> 00:41:30.560
+um what we do is we sample in order to
+
+00:41:26.880 --> 00:41:35.520
+make this trct so a very simple way to
+
+00:41:30.560 --> 00:41:37.640
+minimize risk is instead of um instead
+
+00:41:35.520 --> 00:41:39.359
+of summing over all of the possible
+
+00:41:37.640 --> 00:41:42.760
+outputs we sum over a small number of
+
+00:41:39.359 --> 00:41:46.079
+possible outputs and we upgrade uh and
+
+00:41:42.760 --> 00:41:47.359
+we uh sorry normalize uh to make this
+
+00:41:46.079 --> 00:41:51.200
+all add up to
+
+00:41:47.359 --> 00:41:52.839
+one and so this normalizer here is
+
+00:41:51.200 --> 00:41:55.319
+basically the sum over all of the
+
+00:41:52.839 --> 00:41:58.599
+probabilities that we have uh on the top
+
+00:41:55.319 --> 00:42:02.119
+part here and and these samples can be
+
+00:41:58.599 --> 00:42:05.480
+created either using sampling or n best
+
+00:42:02.119 --> 00:42:07.040
+search we don't need to have from the
+
+00:42:05.480 --> 00:42:11.040
+point of view of doing this sort of
+
+00:42:07.040 --> 00:42:13.960
+minimum risk training the kind of
+
+00:42:11.040 --> 00:42:16.880
+correct way of doing this is sampling
+
+00:42:13.960 --> 00:42:19.880
+using ancestral sampling uh like we
+
+00:42:16.880 --> 00:42:23.079
+talked about before and um in minimizing
+
+00:42:19.880 --> 00:42:25.839
+the output based on the the samples but
+
+00:42:23.079 --> 00:42:28.480
+the problem with that is um as many of
+
+00:42:25.839 --> 00:42:31.440
+you also might have seen when you were
+
+00:42:28.480 --> 00:42:33.599
+sampling from your language model uh
+
+00:42:31.440 --> 00:42:35.160
+from assignment one if you sample with
+
+00:42:33.599 --> 00:42:38.040
+temperature one it gives you a lot of
+
+00:42:35.160 --> 00:42:40.720
+like not very good outlets right and so
+
+00:42:38.040 --> 00:42:43.400
+if you're sampling with temperature one
+
+00:42:40.720 --> 00:42:45.000
+um you'll be exploring a a very large
+
+00:42:43.400 --> 00:42:47.880
+part of the space that actually isn't
+
+00:42:45.000 --> 00:42:49.720
+very good and so because of this uh some
+
+00:42:47.880 --> 00:42:51.480
+other Alternatives that you can use is
+
+00:42:49.720 --> 00:42:53.400
+you can just do endb search to find the
+
+00:42:51.480 --> 00:42:55.280
+best outputs or you can sample with a
+
+00:42:53.400 --> 00:42:58.079
+temperature that's not one or something
+
+00:42:55.280 --> 00:43:00.240
+like that and basically create uh you
+
+00:42:58.079 --> 00:43:02.520
+know a list of possible hypotheses and
+
+00:43:00.240 --> 00:43:04.079
+then normalize other B so that's another
+
+00:43:02.520 --> 00:43:06.240
+option and very often not using
+
+00:43:04.079 --> 00:43:11.200
+temperature one is a better
+
+00:43:06.240 --> 00:43:15.280
+way um if you're sampling with not
+
+00:43:11.200 --> 00:43:18.640
+temperature one and you are um
+
+00:43:15.280 --> 00:43:20.920
+potentially getting multiple outputs you
+
+00:43:18.640 --> 00:43:23.400
+should try to D duplicate or sample
+
+00:43:20.920 --> 00:43:25.480
+without replacement because if you get
+
+00:43:23.400 --> 00:43:27.559
+multiple outputs here it messes up your
+
+00:43:25.480 --> 00:43:30.680
+equations if you basically uh have the
+
+00:43:27.559 --> 00:43:30.680
+same one in there multiple
+
+00:43:32.160 --> 00:43:37.800
+times cool so so this is a really simple
+
+00:43:35.880 --> 00:43:40.079
+example of how you can do minimal risk
+
+00:43:37.800 --> 00:43:42.119
+training but now I want to get into uh
+
+00:43:40.079 --> 00:43:44.640
+like reinforcement learning which is the
+
+00:43:42.119 --> 00:43:48.119
+framing that most um
+
+00:43:44.640 --> 00:43:50.760
+modern Works about this Paulo uh one
+
+00:43:48.119 --> 00:43:52.559
+thing I should mention is there are
+
+00:43:50.760 --> 00:43:55.240
+actually other alternatives to learning
+
+00:43:52.559 --> 00:43:57.359
+from uh human feedback including like
+
+00:43:55.240 --> 00:43:59.359
+margin loss margin based losses and
+
+00:43:57.359 --> 00:44:00.960
+other stuff like that but most people
+
+00:43:59.359 --> 00:44:03.440
+nowadays use reinforcement learning so
+
+00:44:00.960 --> 00:44:06.359
+I'm only going to cover that
+
+00:44:03.440 --> 00:44:08.440
+here so what is reinforcement learning
+
+00:44:06.359 --> 00:44:11.000
+um learning reinforcement learning is
+
+00:44:08.440 --> 00:44:14.559
+learning where we have an environment uh
+
+00:44:11.000 --> 00:44:16.079
+x uh ability to make actions a and get a
+
+00:44:14.559 --> 00:44:20.160
+delayed reward
+
+00:44:16.079 --> 00:44:21.880
+R and um there's a really nice example
+
+00:44:20.160 --> 00:44:24.400
+uh if you're not familiar with the
+
+00:44:21.880 --> 00:44:27.480
+basics of policy gradient by Andre
+
+00:44:24.400 --> 00:44:28.800
+karpathy which I linked in the um in the
+
+00:44:27.480 --> 00:44:29.680
+recommended reading so you can take a
+
+00:44:28.800 --> 00:44:34.680
+look at
+
+00:44:29.680 --> 00:44:37.240
+that um but in that example gives an
+
+00:44:34.680 --> 00:44:39.440
+example of pong uh where you're playing
+
+00:44:37.240 --> 00:44:42.640
+the game pong where X is your observed
+
+00:44:39.440 --> 00:44:45.640
+image a is up or down and R is the wind
+
+00:44:42.640 --> 00:44:47.480
+loss at the end of the game uh does
+
+00:44:45.640 --> 00:44:50.559
+anyone have an idea about uh what this
+
+00:44:47.480 --> 00:44:52.119
+looks like for any arbitrary NLP task
+
+00:44:50.559 --> 00:44:56.520
+that we might want to do reinforcement
+
+00:44:52.119 --> 00:44:59.040
+learning for so what what is X what is a
+
+00:44:56.520 --> 00:44:59.040
+and what is
+
+00:45:00.040 --> 00:45:04.680
+are pick your favorite uh your favorite
+
+00:45:06.920 --> 00:45:09.920
+Trask
+
+00:45:10.960 --> 00:45:18.400
+anybody
+
+00:45:12.520 --> 00:45:18.400
+yeah be or what what's X first
+
+00:45:19.680 --> 00:45:28.720
+yeah you have generate okay is the
+
+00:45:24.440 --> 00:45:29.720
+next be like the Buton like whether or
+
+00:45:28.720 --> 00:45:32.520
+not
+
+00:45:29.720 --> 00:45:35.240
+you okay yeah I I think this is very
+
+00:45:32.520 --> 00:45:37.119
+close just to repeat it it's like X is
+
+00:45:35.240 --> 00:45:39.599
+what you've generated so far a is the
+
+00:45:37.119 --> 00:45:41.559
+next token and R is the button that the
+
+00:45:39.599 --> 00:45:45.400
+user clicks about whether it's good or
+
+00:45:41.559 --> 00:45:46.920
+not um I think that's reasonably good
+
+00:45:45.400 --> 00:45:48.760
+although I don't know if we'd expect
+
+00:45:46.920 --> 00:45:52.960
+them to click the button every token we
+
+00:45:48.760 --> 00:45:54.880
+generate right so um it might be that X
+
+00:45:52.960 --> 00:45:57.880
+is the conversational history up till
+
+00:45:54.880 --> 00:46:02.319
+this point um a
+
+00:45:57.880 --> 00:46:04.280
+a could be a next token generation and
+
+00:46:02.319 --> 00:46:06.520
+then R is a reward we get in an
+
+00:46:04.280 --> 00:46:08.280
+arbitrary time point it might not be
+
+00:46:06.520 --> 00:46:09.960
+like immediately after generating the
+
+00:46:08.280 --> 00:46:12.040
+next token but it might be later and
+
+00:46:09.960 --> 00:46:13.480
+that's actually really really important
+
+00:46:12.040 --> 00:46:15.040
+from the point of view of reinforcement
+
+00:46:13.480 --> 00:46:19.599
+learning and I'll I'll talk about that
+
+00:46:15.040 --> 00:46:23.040
+in a second um anyone have an idea from
+
+00:46:19.599 --> 00:46:24.960
+I don't know uh code generation or
+
+00:46:23.040 --> 00:46:28.119
+translation or some other
+
+00:46:24.960 --> 00:46:31.160
+things C generation maybe s is a
+
+00:46:28.119 --> 00:46:33.040
+compiler or like the gra scpt and then
+
+00:46:31.160 --> 00:46:37.000
+the
+
+00:46:33.040 --> 00:46:42.520
+is the actual code that right and reward
+
+00:46:37.000 --> 00:46:44.839
+is yep um so X could be the compiler
+
+00:46:42.520 --> 00:46:47.559
+it's probably the compiler and all of
+
+00:46:44.839 --> 00:46:50.200
+the surrounding code context like what
+
+00:46:47.559 --> 00:46:52.520
+what is the natural language output and
+
+00:46:50.200 --> 00:46:53.960
+it's also um you know what is the
+
+00:46:52.520 --> 00:46:57.280
+project that you're you're working on
+
+00:46:53.960 --> 00:47:00.079
+and stuff like that um a i think
+
+00:46:57.280 --> 00:47:02.800
+typically we would treat each token in
+
+00:47:00.079 --> 00:47:04.160
+the code to be an action um and then R
+
+00:47:02.800 --> 00:47:06.599
+would be the reward after a long
+
+00:47:04.160 --> 00:47:08.640
+sequence of actions um and it could be
+
+00:47:06.599 --> 00:47:11.119
+the reward from the compiler it could be
+
+00:47:08.640 --> 00:47:13.160
+the reward from a code readability model
+
+00:47:11.119 --> 00:47:15.720
+it could be the reward from a speed
+
+00:47:13.160 --> 00:47:17.079
+execution speed and stuff like that so
+
+00:47:15.720 --> 00:47:18.839
+like one of the interesting things about
+
+00:47:17.079 --> 00:47:22.640
+R is you can be really creative about
+
+00:47:18.839 --> 00:47:25.400
+how you form R um which is not easy to
+
+00:47:22.640 --> 00:47:27.319
+do uh if you're just doing maximum
+
+00:47:25.400 --> 00:47:29.240
+likelihood also so you can come up with
+
+00:47:27.319 --> 00:47:32.920
+a r that really matches with like what
+
+00:47:29.240 --> 00:47:36.559
+you want um what you want in an output
+
+00:47:32.920 --> 00:47:40.079
+so why reinforcement learning in NLP um
+
+00:47:36.559 --> 00:47:42.599
+and I think there's basically three um
+
+00:47:40.079 --> 00:47:44.240
+three answers the first one is you have
+
+00:47:42.599 --> 00:47:49.000
+a typical reinforcement learning
+
+00:47:44.240 --> 00:47:51.119
+scenario um where you have a dialogue
+
+00:47:49.000 --> 00:47:52.720
+where you get lots of responses and then
+
+00:47:51.119 --> 00:47:54.559
+you get a reward at the end so the
+
+00:47:52.720 --> 00:47:57.359
+thumbs up and thumbs down from humans is
+
+00:47:54.559 --> 00:47:59.839
+a very typical example of
+
+00:47:57.359 --> 00:48:02.800
+uh reinforcement learning because you
+
+00:47:59.839 --> 00:48:05.000
+get a delayed reward uh at some point in
+
+00:48:02.800 --> 00:48:07.599
+the dialogue when a human presses up or
+
+00:48:05.000 --> 00:48:09.280
+down um another like actually more
+
+00:48:07.599 --> 00:48:11.680
+technical scenario where reinforcement
+
+00:48:09.280 --> 00:48:14.960
+learning has been used um for a long
+
+00:48:11.680 --> 00:48:17.400
+time is call centers so we've had
+
+00:48:14.960 --> 00:48:20.680
+dialogue systems for call centers and
+
+00:48:17.400 --> 00:48:23.160
+then if you complete a ticket purchase
+
+00:48:20.680 --> 00:48:24.839
+um or you complete resolve a ticket
+
+00:48:23.160 --> 00:48:27.480
+without ever having to go to a human
+
+00:48:24.839 --> 00:48:30.800
+operator you get a really big reward
+
+00:48:27.480 --> 00:48:33.640
+if you have to go to the human operator
+
+00:48:30.800 --> 00:48:36.400
+you get maybe a smaller reward and if
+
+00:48:33.640 --> 00:48:39.200
+the person yells at you and hangs up
+
+00:48:36.400 --> 00:48:41.640
+then you get a really negative reward so
+
+00:48:39.200 --> 00:48:43.040
+um this is kind of the typical example
+
+00:48:41.640 --> 00:48:45.599
+reinforcement learning has been used for
+
+00:48:43.040 --> 00:48:48.520
+a long time there another example is if
+
+00:48:45.599 --> 00:48:53.280
+you have like latent variables uh chains
+
+00:48:48.520 --> 00:48:55.799
+of thought where um you decide the
+
+00:48:53.280 --> 00:48:58.839
+latent variable and then get a reward um
+
+00:48:55.799 --> 00:49:02.799
+you get a reward based Bas on how those
+
+00:48:58.839 --> 00:49:03.920
+latent variables affect the output so um
+
+00:49:02.799 --> 00:49:07.200
+this
+
+00:49:03.920 --> 00:49:09.799
+is uh this is another example
+
+00:49:07.200 --> 00:49:12.599
+because the Chain of Thought itself
+
+00:49:09.799 --> 00:49:13.880
+might not actually be good you might
+
+00:49:12.599 --> 00:49:15.839
+have a bad Chain of Thought and still
+
+00:49:13.880 --> 00:49:17.760
+get the correct answer so you don't
+
+00:49:15.839 --> 00:49:19.640
+actually know for sure that a chain of
+
+00:49:17.760 --> 00:49:22.359
+thought that was automatically generated
+
+00:49:19.640 --> 00:49:24.799
+is good or not but um that so that kind
+
+00:49:22.359 --> 00:49:27.000
+of makes it a reinforcement learning
+
+00:49:24.799 --> 00:49:29.520
+problem and another thing is you might
+
+00:49:27.000 --> 00:49:32.520
+have a sequence level evaluation metric
+
+00:49:29.520 --> 00:49:34.240
+um so that you can't optimize the
+
+00:49:32.520 --> 00:49:36.839
+evaluation metric without uh first
+
+00:49:34.240 --> 00:49:38.480
+generating the whole like sequence so
+
+00:49:36.839 --> 00:49:40.880
+that would be any of the evaluation
+
+00:49:38.480 --> 00:49:42.400
+metrics that I talked about before so um
+
+00:49:40.880 --> 00:49:44.720
+these are three scenarios where you can
+
+00:49:42.400 --> 00:49:47.079
+use reinforcement
+
+00:49:44.720 --> 00:49:50.000
+planning so
+
+00:49:47.079 --> 00:49:51.400
+um I'm going to make a few steps through
+
+00:49:50.000 --> 00:49:54.640
+but like let's start again with our
+
+00:49:51.400 --> 00:49:57.359
+supervised mle loss and uh that's just
+
+00:49:54.640 --> 00:50:01.799
+the log probability here um in the
+
+00:49:57.359 --> 00:50:04.160
+context of reinforcement learning this
+
+00:50:01.799 --> 00:50:07.079
+is also called imitation
+
+00:50:04.160 --> 00:50:08.880
+learning because um essentially you're
+
+00:50:07.079 --> 00:50:12.680
+learning how to perform actions by
+
+00:50:08.880 --> 00:50:14.559
+imitating a teacher um and imitation
+
+00:50:12.680 --> 00:50:15.960
+learning is not just supervised mle
+
+00:50:14.559 --> 00:50:18.440
+there's also other varieties of
+
+00:50:15.960 --> 00:50:21.440
+imitation learning but um this is one
+
+00:50:18.440 --> 00:50:21.440
+variety of imitation
+
+00:50:22.520 --> 00:50:27.640
+learning the next thing I'd like to talk
+
+00:50:24.599 --> 00:50:30.079
+about is self-training and basically
+
+00:50:27.640 --> 00:50:31.760
+self-training the idea is that you
+
+00:50:30.079 --> 00:50:33.720
+sample or argmax according to the
+
+00:50:31.760 --> 00:50:36.119
+current model so you have your current
+
+00:50:33.720 --> 00:50:38.000
+model and you get a sample from it and
+
+00:50:36.119 --> 00:50:41.520
+then you use the sample or samples to
+
+00:50:38.000 --> 00:50:43.680
+maximize likelihood so um basically
+
+00:50:41.520 --> 00:50:47.520
+instead of doing maximum likelihood with
+
+00:50:43.680 --> 00:50:49.520
+respect to the a gold standard output
+
+00:50:47.520 --> 00:50:51.280
+you're doing it with respect to your own
+
+00:50:49.520 --> 00:50:55.280
+output
+
+00:50:51.280 --> 00:50:55.280
+so does this seem like a good
+
+00:50:55.640 --> 00:51:03.880
+idea I see a few people shaking heads um
+
+00:51:00.480 --> 00:51:03.880
+any ideas why this is not a good
+
+00:51:04.680 --> 00:51:07.680
+idea
+
+00:51:15.040 --> 00:51:20.599
+yeah yeah exactly so if you don't have
+
+00:51:17.720 --> 00:51:23.760
+any access to any notion well it's good
+
+00:51:20.599 --> 00:51:27.480
+um this will be optimizing towards good
+
+00:51:23.760 --> 00:51:28.839
+outputs and bad outputs right so um your
+
+00:51:27.480 --> 00:51:30.200
+model might be outputting bad outputs
+
+00:51:28.839 --> 00:51:32.839
+and you're just reinforcing the errors
+
+00:51:30.200 --> 00:51:35.160
+set the model R already nonetheless like
+
+00:51:32.839 --> 00:51:37.799
+self trining actually improves your
+
+00:51:35.160 --> 00:51:39.680
+accuracy somewhat in some cases like for
+
+00:51:37.799 --> 00:51:43.040
+example if your accuracy is if your
+
+00:51:39.680 --> 00:51:45.520
+model is Right more often than not um
+
+00:51:43.040 --> 00:51:49.119
+basically optimizing towards the more
+
+00:51:45.520 --> 00:51:51.720
+often the not right outputs can actually
+
+00:51:49.119 --> 00:51:53.640
+um due to the implicit regularization
+
+00:51:51.720 --> 00:51:55.000
+that models have and early stopping and
+
+00:51:53.640 --> 00:51:56.559
+other things like that it can actually
+
+00:51:55.000 --> 00:51:59.280
+move you in the right direction and
+
+00:51:56.559 --> 00:52:01.559
+improve accuracy
+
+00:51:59.280 --> 00:52:05.000
+um
+
+00:52:01.559 --> 00:52:06.640
+so there are alternatives to this that
+
+00:52:05.000 --> 00:52:09.520
+further improve accuracy so like for
+
+00:52:06.640 --> 00:52:12.720
+example if you have multiple models and
+
+00:52:09.520 --> 00:52:16.200
+um you only generate sentences where the
+
+00:52:12.720 --> 00:52:17.760
+models agree then this can improve your
+
+00:52:16.200 --> 00:52:20.000
+uh overall accuracy
+
+00:52:17.760 --> 00:52:24.240
+further um this is called code training
+
+00:52:20.000 --> 00:52:27.799
+it was actually uh created by uh uh
+
+00:52:24.240 --> 00:52:30.160
+people at at CMU as well and another
+
+00:52:27.799 --> 00:52:32.280
+successful alternative uh is adding
+
+00:52:30.160 --> 00:52:34.920
+noise to the input to match the noise
+
+00:52:32.280 --> 00:52:38.760
+that you find in the output so if you uh
+
+00:52:34.920 --> 00:52:40.720
+add like word uh word-based Dropout or
+
+00:52:38.760 --> 00:52:44.000
+other things like that this can also
+
+00:52:40.720 --> 00:52:47.400
+help uh accommodate these things but
+
+00:52:44.000 --> 00:52:48.920
+anyway um so self trining is is useful
+
+00:52:47.400 --> 00:52:50.480
+but there are better Alternatives if you
+
+00:52:48.920 --> 00:52:54.079
+can get a reward
+
+00:52:50.480 --> 00:52:55.559
+function so um the simplest variety of
+
+00:52:54.079 --> 00:52:56.960
+this is something called policy gradient
+
+00:52:55.559 --> 00:52:59.720
+or reinforce
+
+00:52:56.960 --> 00:53:02.319
+um or more specifically reinforce and
+
+00:52:59.720 --> 00:53:06.280
+basically what this does is this adds a
+
+00:53:02.319 --> 00:53:08.359
+term that scales the loss by the reward
+
+00:53:06.280 --> 00:53:12.400
+so if you can get a reward for each
+
+00:53:08.359 --> 00:53:15.680
+output basically this
+
+00:53:12.400 --> 00:53:18.119
+um you uh instead of doing self trining
+
+00:53:15.680 --> 00:53:21.760
+entirely by itself you multiply it by a
+
+00:53:18.119 --> 00:53:23.119
+reward and this allows you to increase
+
+00:53:21.760 --> 00:53:24.640
+the likelihood of things that get a high
+
+00:53:23.119 --> 00:53:28.440
+reward decrease the likelihood of things
+
+00:53:24.640 --> 00:53:28.440
+that get a low reward
+
+00:53:29.680 --> 00:53:34.960
+so uh a brief quiz here under what
+
+00:53:32.440 --> 00:53:37.599
+conditions is this equal equivalent to
+
+00:53:34.960 --> 00:53:41.480
+ml or essentially equivalent to maximum
+
+00:53:37.599 --> 00:53:43.079
+leg uh estimation and so like in order
+
+00:53:41.480 --> 00:53:45.480
+to make this quiz easier I'll go back to
+
+00:53:43.079 --> 00:53:47.720
+maximum likelihood estimation so it
+
+00:53:45.480 --> 00:53:50.359
+looked a bit like this um you calculated
+
+00:53:47.720 --> 00:53:53.440
+the log probability of the true output
+
+00:53:50.359 --> 00:53:55.440
+and now let me go uh to
+
+00:53:53.440 --> 00:53:56.960
+here any
+
+00:53:55.440 --> 00:54:00.119
+ideas
+
+00:53:56.960 --> 00:54:05.040
+yeah when your reward equals to
+
+00:54:00.119 --> 00:54:05.040
+one some sometimes in zero other times
+
+00:54:07.760 --> 00:54:10.960
+what any
+
+00:54:12.760 --> 00:54:17.520
+ideas what when when does your reward
+
+00:54:15.280 --> 00:54:19.640
+need to be equal to one in order to make
+
+00:54:17.520 --> 00:54:23.400
+this
+
+00:54:19.640 --> 00:54:23.400
+equation equivalent this
+
+00:54:24.960 --> 00:54:31.680
+equation yeah when Y and Y hat are the
+
+00:54:27.319 --> 00:54:36.119
+same so um basically
+
+00:54:31.680 --> 00:54:38.880
+this objective is equivalent to the mle
+
+00:54:36.119 --> 00:54:43.160
+objective when you're using a zero1
+
+00:54:38.880 --> 00:54:44.480
+loss um where or you're using an
+
+00:54:43.160 --> 00:54:46.359
+evaluation function that gives you a
+
+00:54:44.480 --> 00:54:50.920
+score of one when it's exact match and
+
+00:54:46.359 --> 00:54:51.720
+zero when it's not exact match so um but
+
+00:54:50.920 --> 00:54:54.480
+that
+
+00:54:51.720 --> 00:54:56.440
+also demonstrates that this can be more
+
+00:54:54.480 --> 00:54:58.400
+flexible because you can have other
+
+00:54:56.440 --> 00:55:00.160
+rewards that are not just one and zero
+
+00:54:58.400 --> 00:55:02.599
+for exact match but you can use things
+
+00:55:00.160 --> 00:55:05.359
+that give you partial credit you can use
+
+00:55:02.599 --> 00:55:06.880
+things that uplate multiple potential uh
+
+00:55:05.359 --> 00:55:08.880
+potentially correct outputs and other
+
+00:55:06.880 --> 00:55:13.400
+things like
+
+00:55:08.880 --> 00:55:17.160
+that so one problem with these methods
+
+00:55:13.400 --> 00:55:21.799
+is um how do we know which action led to
+
+00:55:17.160 --> 00:55:24.720
+the reward so the best scenario is after
+
+00:55:21.799 --> 00:55:26.359
+each action you get a reward so after
+
+00:55:24.720 --> 00:55:28.960
+each token that you generated you get
+
+00:55:26.359 --> 00:55:31.240
+get a thumbs up or thumbs down uh from
+
+00:55:28.960 --> 00:55:34.280
+the user about whether they like that
+
+00:55:31.240 --> 00:55:36.000
+token or not um and how much happier
+
+00:55:34.280 --> 00:55:37.720
+they are after you generated that token
+
+00:55:36.000 --> 00:55:42.400
+than they were before you generated that
+
+00:55:37.720 --> 00:55:44.200
+token um the problem with this is that
+
+00:55:42.400 --> 00:55:45.799
+that's completely infeasible right like
+
+00:55:44.200 --> 00:55:47.039
+every time after you use chat GPD you're
+
+00:55:45.799 --> 00:55:50.480
+not going to press thumbs up and thumbs
+
+00:55:47.039 --> 00:55:52.559
+down after each token so um in reality
+
+00:55:50.480 --> 00:55:55.559
+what we get is usually we get it at the
+
+00:55:52.559 --> 00:55:57.000
+end of uh roll out of many many
+
+00:55:55.559 --> 00:55:58.640
+different actions and we're not sure
+
+00:55:57.000 --> 00:55:59.720
+which action is responsible for giving
+
+00:55:58.640 --> 00:56:02.559
+us the
+
+00:55:59.720 --> 00:56:05.440
+reward and
+
+00:56:02.559 --> 00:56:08.000
+so there's a few typical ways of dealing
+
+00:56:05.440 --> 00:56:09.640
+with this um the most typical way of
+
+00:56:08.000 --> 00:56:13.359
+dealing with this right now is just not
+
+00:56:09.640 --> 00:56:15.440
+dealing with it um and just hoping that
+
+00:56:13.359 --> 00:56:17.200
+your optimization algorithm internally
+
+00:56:15.440 --> 00:56:21.480
+will be able to do credit
+
+00:56:17.200 --> 00:56:24.520
+assignment um and so what that entails
+
+00:56:21.480 --> 00:56:27.319
+is essentially you um give an equal
+
+00:56:24.520 --> 00:56:29.880
+reward for each token in the output
+
+00:56:27.319 --> 00:56:32.480
+other ways that you can deal with it are
+
+00:56:29.880 --> 00:56:35.640
+um you can assign decaying rewards from
+
+00:56:32.480 --> 00:56:37.559
+future events so like let's say let's
+
+00:56:35.640 --> 00:56:41.839
+say you're talking about a chat bot for
+
+00:56:37.559 --> 00:56:44.119
+example maybe this is the the most uh
+
+00:56:41.839 --> 00:56:46.599
+kind of intuitive way of thinking about
+
+00:56:44.119 --> 00:56:50.400
+it but you you have a chat bot you have
+
+00:56:46.599 --> 00:56:52.599
+like 20 chat turns and you have the user
+
+00:56:50.400 --> 00:56:55.640
+give a thumbs up or a thumbs down on the
+
+00:56:52.599 --> 00:56:58.920
+20th chat turn there you would assign a
+
+00:56:55.640 --> 00:57:01.440
+reward of um like let's say it gave a
+
+00:56:58.920 --> 00:57:03.640
+thumbs up there you would re assign a
+
+00:57:01.440 --> 00:57:06.559
+reward of one for the previous chat turn
+
+00:57:03.640 --> 00:57:09.839
+a reward of like 0.5 for the second to
+
+00:57:06.559 --> 00:57:11.720
+previous chat term a reward of 0.25 for
+
+00:57:09.839 --> 00:57:14.319
+the third to previous chat term to
+
+00:57:11.720 --> 00:57:16.160
+basically say yeah like the user is
+
+00:57:14.319 --> 00:57:18.240
+feeling good at the moment they gave the
+
+00:57:16.160 --> 00:57:20.359
+thumbs up and that's probably more
+
+00:57:18.240 --> 00:57:23.400
+likely due to the things that happened
+
+00:57:20.359 --> 00:57:23.400
+recently so
+
+00:57:23.559 --> 00:57:28.119
+yeah we have a
+
+00:57:26.680 --> 00:57:32.280
+like not
+
+00:57:28.119 --> 00:57:34.160
+learning so the reward model can be any
+
+00:57:32.280 --> 00:57:35.839
+of the methods that I talked about
+
+00:57:34.160 --> 00:57:37.480
+before so it can be human feedback
+
+00:57:35.839 --> 00:57:39.000
+directly like a thumbs up or a thumbs
+
+00:57:37.480 --> 00:57:42.200
+down it could also be from a reward
+
+00:57:39.000 --> 00:57:44.599
+model uh that was pre-trained you could
+
+00:57:42.200 --> 00:57:47.680
+also theoretically learn the reward
+
+00:57:44.599 --> 00:57:52.720
+model simultaneously but you'd have to
+
+00:57:47.680 --> 00:57:55.200
+simultaneously with the model itself um
+
+00:57:52.720 --> 00:57:57.280
+so yeah I'm going to talk a little bit
+
+00:57:55.200 --> 00:58:00.359
+about DP which kind of does that a
+
+00:57:57.280 --> 00:58:01.720
+little bit but um I I would basically
+
+00:58:00.359 --> 00:58:03.160
+say that wherever you're getting your
+
+00:58:01.720 --> 00:58:06.280
+reward is probably from one of the
+
+00:58:03.160 --> 00:58:06.280
+things I talked about earlier
+
+00:58:06.359 --> 00:58:14.960
+today cool any other
+
+00:58:09.319 --> 00:58:17.720
+questions okay um so that's the basic
+
+00:58:14.960 --> 00:58:20.640
+the basic idea the very simplest thing
+
+00:58:17.720 --> 00:58:23.359
+that you can do is you can just sample
+
+00:58:20.640 --> 00:58:26.079
+um optimize the subjective function this
+
+00:58:23.359 --> 00:58:28.359
+is dead easy you it's not hard to imp
+
+00:58:26.079 --> 00:58:30.799
+imp it all as long as you have some
+
+00:58:28.359 --> 00:58:32.760
+source of reward signal um but the
+
+00:58:30.799 --> 00:58:35.559
+problem is uh reinforcement learning can
+
+00:58:32.760 --> 00:58:38.599
+be very unstable and it's hard to get it
+
+00:58:35.559 --> 00:58:40.160
+to uh you know work properly if you uh
+
+00:58:38.599 --> 00:58:42.400
+don't do some additional tricks so I'd
+
+00:58:40.160 --> 00:58:45.720
+like to talk about this
+
+00:58:42.400 --> 00:58:45.720
+next oh yeah
+
+00:58:48.880 --> 00:58:51.880
+sir
+
+00:58:55.039 --> 00:58:58.039
+yeah
+
+00:59:03.280 --> 00:59:08.960
+yeah the typical the typical way is you
+
+00:59:05.440 --> 00:59:12.960
+just have an exponential decay um so you
+
+00:59:08.960 --> 00:59:16.200
+you multiply each time by what 0.5 0. or
+
+00:59:12.960 --> 00:59:19.400
+something like that
+
+00:59:16.200 --> 00:59:19.400
+um from
+
+00:59:20.319 --> 00:59:27.720
+A6 um cool okay
+
+00:59:25.039 --> 00:59:30.720
+so
+
+00:59:27.720 --> 00:59:33.319
+and that's one option and sorry just to
+
+00:59:30.720 --> 00:59:35.760
+clarify the most common option nowadays
+
+00:59:33.319 --> 00:59:37.920
+um at least from the point of view of
+
+00:59:35.760 --> 00:59:39.839
+models is not to Decay it at all and
+
+00:59:37.920 --> 00:59:43.880
+just assign the same amount for each
+
+00:59:39.839 --> 00:59:45.319
+token um I'm not actually 100% sure what
+
+00:59:43.880 --> 00:59:47.319
+people are doing with respect to like
+
+00:59:45.319 --> 00:59:49.280
+long chat things I think probably
+
+00:59:47.319 --> 00:59:51.720
+they're only assigning it to the current
+
+00:59:49.280 --> 00:59:54.240
+like utterance and then not optimizing
+
+00:59:51.720 --> 00:59:57.240
+the previous utterances so like if they
+
+00:59:54.240 --> 00:59:59.039
+get a thumbs up or thumbs down signal um
+
+00:59:57.240 --> 01:00:00.720
+then they they would assign an
+
+00:59:59.039 --> 01:00:02.440
+equivalent reward for all of the tokens
+
+01:00:00.720 --> 01:00:04.640
+and the current utterance and zero
+
+01:00:02.440 --> 01:00:06.119
+reward for the previous ones but I'm not
+
+01:00:04.640 --> 01:00:08.480
+100% sure about that there might be
+
+01:00:06.119 --> 01:00:11.200
+other methods that people are
+
+01:00:08.480 --> 01:00:13.960
+using um
+
+01:00:11.200 --> 01:00:16.680
+cool so uh stabilizing reinforcement
+
+01:00:13.960 --> 01:00:18.520
+learning so um stabilizing reinforcement
+
+01:00:16.680 --> 01:00:21.839
+learning there's a lot of reasons why
+
+01:00:18.520 --> 01:00:23.880
+it's unstable um the first reason is
+
+01:00:21.839 --> 01:00:27.200
+you're sampling an individual output and
+
+01:00:23.880 --> 01:00:30.160
+calculating the um uh calculating based
+
+01:00:27.200 --> 01:00:32.039
+on the S individual sampled output and
+
+01:00:30.160 --> 01:00:33.440
+then there's an Infinity of other
+
+01:00:32.039 --> 01:00:36.480
+outputs that you could be optimizing
+
+01:00:33.440 --> 01:00:39.119
+over for mle this is not a problem
+
+01:00:36.480 --> 01:00:41.319
+because for mle you're always
+
+01:00:39.119 --> 01:00:45.359
+contrasting the gold standard output to
+
+01:00:41.319 --> 01:00:46.599
+all of the other outputs in the space um
+
+01:00:45.359 --> 01:00:48.280
+and you're saying I want to upweight the
+
+01:00:46.599 --> 01:00:51.200
+gold standard output and down we all of
+
+01:00:48.280 --> 01:00:53.039
+the other ones but for reinforcement
+
+01:00:51.200 --> 01:00:54.760
+learning you only have a single sampled
+
+01:00:53.039 --> 01:00:57.520
+output that output might be wrong and
+
+01:00:54.760 --> 01:00:59.359
+that's a source of inst ility this is
+
+01:00:57.520 --> 01:01:02.079
+particularly a problem when using bigger
+
+01:00:59.359 --> 01:01:05.960
+output spaces like all of the in the
+
+01:01:02.079 --> 01:01:07.920
+vocabul another problem is uh anytime
+
+01:01:05.960 --> 01:01:11.599
+you start using negative
+
+01:01:07.920 --> 01:01:15.160
+rewards um because if you start using
+
+01:01:11.599 --> 01:01:17.559
+negative rewards those rewards will be
+
+01:01:15.160 --> 01:01:19.520
+downweighting the probability of a
+
+01:01:17.559 --> 01:01:20.680
+particular output sequence and that
+
+01:01:19.520 --> 01:01:22.440
+might be a good idea maybe you're
+
+01:01:20.680 --> 01:01:24.319
+getting a toxic output or something like
+
+01:01:22.440 --> 01:01:25.960
+that and you want to down it but at the
+
+01:01:24.319 --> 01:01:28.280
+same time in addition to that toxic
+
+01:01:25.960 --> 01:01:30.000
+output there's like you know a
+
+01:01:28.280 --> 01:01:31.599
+combinatorial number of completely
+
+01:01:30.000 --> 01:01:33.880
+nonsense outputs that aren't even
+
+01:01:31.599 --> 01:01:36.599
+English and so basically you can start
+
+01:01:33.880 --> 01:01:38.920
+diverge from the N starting start to
+
+01:01:36.599 --> 01:01:40.799
+diverge from the natural like language
+
+01:01:38.920 --> 01:01:44.720
+modeling distribution that you have
+
+01:01:40.799 --> 01:01:49.079
+before so this is a big uh a big
+
+01:01:44.720 --> 01:01:51.880
+problem so a number of uh strategies can
+
+01:01:49.079 --> 01:01:53.880
+be used to stabilize the first one is
+
+01:01:51.880 --> 01:01:55.480
+this is completely obvious right now and
+
+01:01:53.880 --> 01:01:57.240
+nobody in their right mind would avoid
+
+01:01:55.480 --> 01:02:00.119
+doing this but the first one is
+
+01:01:57.240 --> 01:02:02.839
+pre-training with mle and so you start
+
+01:02:00.119 --> 01:02:04.920
+with a pre-trained model um and then
+
+01:02:02.839 --> 01:02:09.359
+switch over to RL after you finished
+
+01:02:04.920 --> 01:02:11.520
+pre-training the model um and so
+
+01:02:09.359 --> 01:02:13.279
+this makes a lot of sense if you're
+
+01:02:11.520 --> 01:02:14.960
+training a language model which I assume
+
+01:02:13.279 --> 01:02:17.039
+that almost everybody in this class is
+
+01:02:14.960 --> 01:02:20.279
+going to be doing but it does only work
+
+01:02:17.039 --> 01:02:22.720
+in scenarios where you can run mle and
+
+01:02:20.279 --> 01:02:24.359
+so it doesn't work if you're predicting
+
+01:02:22.720 --> 01:02:27.240
+like latent variables that aren't
+
+01:02:24.359 --> 01:02:28.760
+included in the original space
+
+01:02:27.240 --> 01:02:31.960
+um it
+
+01:02:28.760 --> 01:02:34.279
+also doesn't work in a setting where
+
+01:02:31.960 --> 01:02:36.640
+like you want to learn a
+
+01:02:34.279 --> 01:02:40.799
+chatbot you want to learn a chatbot for
+
+01:02:36.640 --> 01:02:44.200
+customer service for a
+
+01:02:40.799 --> 01:02:48.039
+company that
+
+01:02:44.200 --> 01:02:49.960
+has like for example a product catalog
+
+01:02:48.039 --> 01:02:53.559
+that the language model has never seen
+
+01:02:49.960 --> 01:02:56.000
+before and so if the language model has
+
+01:02:53.559 --> 01:02:57.359
+no information about the product catalog
+
+01:02:56.000 --> 01:02:59.920
+whatsoever you don't provide it through
+
+01:02:57.359 --> 01:03:02.440
+rag or something like that it's going to
+
+01:02:59.920 --> 01:03:04.039
+have to explore infinitely or not
+
+01:03:02.440 --> 01:03:05.599
+infinitely but it's going to have to
+
+01:03:04.039 --> 01:03:08.359
+explore too large of a space and you're
+
+01:03:05.599 --> 01:03:10.000
+never going to converge with um with
+
+01:03:08.359 --> 01:03:12.359
+your language modeling objectives so you
+
+01:03:10.000 --> 01:03:15.000
+need to basically be able to create at
+
+01:03:12.359 --> 01:03:16.079
+least some supervised training data to
+
+01:03:15.000 --> 01:03:19.279
+train with
+
+01:03:16.079 --> 01:03:20.720
+mle um but assuming you can do that I'm
+
+01:03:19.279 --> 01:03:22.920
+assuming that almost everybody is going
+
+01:03:20.720 --> 01:03:26.400
+to do some sort of pre-training with
+
+01:03:22.920 --> 01:03:27.880
+ML um The Next Step that people use uh
+
+01:03:26.400 --> 01:03:30.520
+in reinforcement learning that's really
+
+01:03:27.880 --> 01:03:34.319
+important to stabilize is regularization
+
+01:03:30.520 --> 01:03:35.880
+to an existing model and you have an
+
+01:03:34.319 --> 01:03:39.039
+existing model and you want to prevent
+
+01:03:35.880 --> 01:03:40.559
+it from getting too far away and the
+
+01:03:39.039 --> 01:03:42.279
+reason why you want to do this is like
+
+01:03:40.559 --> 01:03:45.720
+let's say you start assigning a negative
+
+01:03:42.279 --> 01:03:47.440
+reward to toxic utterances for example
+
+01:03:45.720 --> 01:03:49.200
+if your model stops being a language
+
+01:03:47.440 --> 01:03:51.920
+model whatsoever that's a bad idea so
+
+01:03:49.200 --> 01:03:53.400
+you want to keep it as a language model
+
+01:03:51.920 --> 01:03:55.599
+keep it close enough to still being a
+
+01:03:53.400 --> 01:03:57.559
+competent language model while you know
+
+01:03:55.599 --> 01:03:59.599
+like removing the toxic
+
+01:03:57.559 --> 01:04:03.039
+utterances so there's a number of
+
+01:03:59.599 --> 01:04:05.680
+methods that people use to do this um uh
+
+01:04:03.039 --> 01:04:08.359
+the most prominent ones are kale
+
+01:04:05.680 --> 01:04:10.279
+regularization uh well so the the first
+
+01:04:08.359 --> 01:04:13.119
+most prominent one is K regularization
+
+01:04:10.279 --> 01:04:15.839
+and the way this works is basically in
+
+01:04:13.119 --> 01:04:19.400
+addition you add you have two
+
+01:04:15.839 --> 01:04:22.279
+terms the first term is a term that
+
+01:04:19.400 --> 01:04:25.760
+improves your reward so you have your
+
+01:04:22.279 --> 01:04:28.039
+old model where your old model is
+
+01:04:25.760 --> 01:04:31.279
+creating a
+
+01:04:28.039 --> 01:04:32.440
+probability uh it has a probability here
+
+01:04:31.279 --> 01:04:34.960
+and then you have the probability
+
+01:04:32.440 --> 01:04:38.160
+assigned by your new model and then you
+
+01:04:34.960 --> 01:04:41.200
+have your reward signal here and so this
+
+01:04:38.160 --> 01:04:43.599
+is basically improving the log odds or
+
+01:04:41.200 --> 01:04:46.960
+improving the odds of getting a good
+
+01:04:43.599 --> 01:04:49.720
+reward for high reward
+
+01:04:46.960 --> 01:04:52.920
+sequences separately from this you have
+
+01:04:49.720 --> 01:04:55.920
+this K regularization term and this K
+
+01:04:52.920 --> 01:04:58.119
+regularization term is keeping the
+
+01:04:55.920 --> 01:05:00.279
+scores of or it's keeping the
+
+01:04:58.119 --> 01:05:02.400
+probability distribution of your new
+
+01:05:00.279 --> 01:05:03.960
+model similar to the probability
+
+01:05:02.400 --> 01:05:09.200
+distribution of your old
+
+01:05:03.960 --> 01:05:11.359
+model and this beta parameter basically
+
+01:05:09.200 --> 01:05:15.240
+you can increase it or decrease it based
+
+01:05:11.359 --> 01:05:18.400
+on how similar you want to keep the um
+
+01:05:15.240 --> 01:05:18.400
+how similar you want to keep the
+
+01:05:20.720 --> 01:05:24.640
+model another method that people use is
+
+01:05:23.160 --> 01:05:29.279
+something called proximal policy
+
+01:05:24.640 --> 01:05:30.920
+optimization or or Po and this is a
+
+01:05:29.279 --> 01:05:33.920
+method that is based on
+
+01:05:30.920 --> 01:05:38.160
+clipping uh the
+
+01:05:33.920 --> 01:05:40.920
+outputs and we Define uh this ratio
+
+01:05:38.160 --> 01:05:43.880
+here so this ratio is equivalent to this
+
+01:05:40.920 --> 01:05:46.160
+here so it's basically um kind of the
+
+01:05:43.880 --> 01:05:47.839
+amount that you're learning or the
+
+01:05:46.160 --> 01:05:51.720
+amount that the new model up weights
+
+01:05:47.839 --> 01:05:54.039
+High reward sequences and so here we
+
+01:05:51.720 --> 01:05:58.200
+have the same thing that we had
+
+01:05:54.039 --> 01:06:01.200
+above so it it looks like this but over
+
+01:05:58.200 --> 01:06:03.720
+here we have a clipped version of this
+
+01:06:01.200 --> 01:06:07.000
+where essentially what we do is we
+
+01:06:03.720 --> 01:06:07.000
+clip this
+
+01:06:21.119 --> 01:06:27.880
+ratio this ratio to be within uh a
+
+01:06:24.720 --> 01:06:32.160
+certain range of the original ratio and
+
+01:06:27.880 --> 01:06:37.880
+what this is doing is this is
+
+01:06:32.160 --> 01:06:41.400
+essentially forcing the model to um not
+
+01:06:37.880 --> 01:06:44.000
+reward large jumps in the space um
+
+01:06:41.400 --> 01:06:47.559
+because if you take the
+
+01:06:44.000 --> 01:06:49.160
+minimum and actually I'm I'm sorry I
+
+01:06:47.559 --> 01:06:50.720
+just realized I I might have done
+
+01:06:49.160 --> 01:06:52.520
+something confusing here because this is
+
+01:06:50.720 --> 01:06:53.960
+actually higher as better so this isn't
+
+01:06:52.520 --> 01:06:56.079
+really a loss function this is something
+
+01:06:53.960 --> 01:06:57.680
+you're attempting to maximize so
+
+01:06:56.079 --> 01:06:59.839
+in contrast to all of the other things I
+
+01:06:57.680 --> 01:07:01.680
+was talking about before um this is
+
+01:06:59.839 --> 01:07:04.400
+something where higher is better instead
+
+01:07:01.680 --> 01:07:07.599
+of lower is better but anyway basically
+
+01:07:04.400 --> 01:07:09.599
+by taking the minimum of this you're
+
+01:07:07.599 --> 01:07:11.960
+encouraging the model
+
+01:07:09.599 --> 01:07:16.279
+to
+
+01:07:11.960 --> 01:07:18.559
+uh keep examining the space where you
+
+01:07:16.279 --> 01:07:20.799
+don't diverge much from the original
+
+01:07:18.559 --> 01:07:22.920
+model and if the space where the
+
+01:07:20.799 --> 01:07:25.240
+original model was in is better than the
+
+01:07:22.920 --> 01:07:27.440
+new space that your model has moved into
+
+01:07:25.240 --> 01:07:30.920
+you move back towards the original model
+
+01:07:27.440 --> 01:07:33.000
+so basically like if you had um if you
+
+01:07:30.920 --> 01:07:34.960
+learned a model if you started learning
+
+01:07:33.000 --> 01:07:37.960
+a model that looked like it was
+
+01:07:34.960 --> 01:07:40.279
+optimizing uh your your reward but then
+
+01:07:37.960 --> 01:07:43.119
+suddenly the model went off the rails
+
+01:07:40.279 --> 01:07:45.000
+and um it starts generating completely
+
+01:07:43.119 --> 01:07:47.319
+nonsense outputs that get really bad
+
+01:07:45.000 --> 01:07:49.119
+reward this will push it back towards
+
+01:07:47.319 --> 01:07:50.920
+the original policy and that's the basic
+
+01:07:49.119 --> 01:07:54.279
+idea behind
+
+01:07:50.920 --> 01:07:57.640
+P um in terms of what I see people using
+
+01:07:54.279 --> 01:07:59.799
+um po was like really really popular for
+
+01:07:57.640 --> 01:08:01.880
+a while but I've started to see people
+
+01:07:59.799 --> 01:08:04.799
+use alternative strategies that use K
+
+01:08:01.880 --> 01:08:06.880
+regularization so I don't I don't think
+
+01:08:04.799 --> 01:08:08.520
+either one of them is like particularly
+
+01:08:06.880 --> 01:08:10.039
+more popular than any of the others and
+
+01:08:08.520 --> 01:08:13.720
+this one's a little bit simpler
+
+01:08:10.039 --> 01:08:13.720
+conceptually so I like the the
+
+01:08:14.880 --> 01:08:19.279
+one cool um any questions about
+
+01:08:20.359 --> 01:08:26.759
+this okay um and actually one thing I
+
+01:08:24.640 --> 01:08:29.679
+should mention is um all of these things
+
+01:08:26.759 --> 01:08:32.120
+are implemented uh in you know whatever
+
+01:08:29.679 --> 01:08:33.759
+libraries you use like hugging face TRL
+
+01:08:32.120 --> 01:08:35.679
+Transformer reinforcement learning as an
+
+01:08:33.759 --> 01:08:37.040
+example Library all of these methods are
+
+01:08:35.679 --> 01:08:38.400
+implemented there so if you actually
+
+01:08:37.040 --> 01:08:40.600
+want to use these in practice that's
+
+01:08:38.400 --> 01:08:40.600
+good
+
+01:08:40.839 --> 01:08:46.359
+place the next thing is adding a
+
+01:08:42.920 --> 01:08:48.679
+Baseline and so the basic idea is that
+
+01:08:46.359 --> 01:08:52.199
+you have ex expectations about your
+
+01:08:48.679 --> 01:08:54.640
+reward for a particular sentence and um
+
+01:08:52.199 --> 01:08:56.560
+like let's say we wanted to uh translate
+
+01:08:54.640 --> 01:08:58.400
+a sentence and we have uh something like
+
+01:08:56.560 --> 01:09:01.279
+this is an easy sentence and buffalo
+
+01:08:58.400 --> 01:09:02.920
+buffalo buffalo which is a harder
+
+01:09:01.279 --> 01:09:07.799
+sentence to
+
+01:09:02.920 --> 01:09:09.679
+translate and so we have a reward um if
+
+01:09:07.799 --> 01:09:11.759
+if you're not familiar with this example
+
+01:09:09.679 --> 01:09:13.480
+you can search on Wikipedia for buffalo
+
+01:09:11.759 --> 01:09:16.759
+buffalo buffalo and you'll you'll find
+
+01:09:13.480 --> 01:09:19.520
+out what I'm talking about um but uh
+
+01:09:16.759 --> 01:09:21.440
+there's a reward uh and let's say you
+
+01:09:19.520 --> 01:09:24.359
+got a reward of 0.8 for the first one
+
+01:09:21.440 --> 01:09:29.679
+and a reward of 0.3 for the second
+
+01:09:24.359 --> 01:09:31.679
+one but the problem is if um the first
+
+01:09:29.679 --> 01:09:33.640
+one actually is really easy and the
+
+01:09:31.679 --> 01:09:36.120
+second one is really hard getting a
+
+01:09:33.640 --> 01:09:37.799
+reward of 0.8 for the second one for
+
+01:09:36.120 --> 01:09:40.080
+like a translation or something is
+
+01:09:37.799 --> 01:09:41.120
+actually bad right and a reward of 0.3
+
+01:09:40.080 --> 01:09:45.239
+is good because you're moving in the
+
+01:09:41.120 --> 01:09:49.359
+right direction and so you basically um
+
+01:09:45.239 --> 01:09:52.239
+you have uh the Baseline uh minus reward
+
+01:09:49.359 --> 01:09:54.960
+or sorry reward minus Baseline and this
+
+01:09:52.239 --> 01:09:56.520
+would give you a negative value for this
+
+01:09:54.960 --> 01:09:59.320
+first one a positive value for the
+
+01:09:56.520 --> 01:10:01.360
+second one and so the basic idea is can
+
+01:09:59.320 --> 01:10:04.400
+we predict a priori how difficult this
+
+01:10:01.360 --> 01:10:05.440
+example is and then uh adjust our reward
+
+01:10:04.400 --> 01:10:08.360
+based on
+
+01:10:05.440 --> 01:10:10.960
+that and
+
+01:10:08.360 --> 01:10:13.679
+so that's the basic idea you just have
+
+01:10:10.960 --> 01:10:15.560
+kind of like a baseline model um you
+
+01:10:13.679 --> 01:10:19.320
+have a baseline model that predicts this
+
+01:10:15.560 --> 01:10:19.320
+and uh you adjust uh
+
+01:10:19.760 --> 01:10:25.000
+appropriately um there's two major ways
+
+01:10:22.719 --> 01:10:27.600
+you can do this the first one um the
+
+01:10:25.000 --> 01:10:29.800
+Baseline doesn't need to be anything um
+
+01:10:27.600 --> 01:10:32.960
+the only hope is that it decreases the
+
+01:10:29.800 --> 01:10:35.960
+variance in your reward uh and makes
+
+01:10:32.960 --> 01:10:38.239
+learning more stable um there's two
+
+01:10:35.960 --> 01:10:40.159
+options that I see done pretty widely
+
+01:10:38.239 --> 01:10:43.000
+the first one is predicting the final
+
+01:10:40.159 --> 01:10:47.360
+reward um predicting the final reward
+
+01:10:43.000 --> 01:10:50.960
+using a model that doesn't look at
+
+01:10:47.360 --> 01:10:53.400
+all at the answer that you provided it
+
+01:10:50.960 --> 01:10:55.880
+only looks at the input or it only looks
+
+01:10:53.400 --> 01:10:58.840
+at the intermediate States of uh you
+
+01:10:55.880 --> 01:11:00.480
+know a model or something and so at the
+
+01:10:58.840 --> 01:11:03.280
+sentence level you can have one Baseline
+
+01:11:00.480 --> 01:11:04.719
+per sentence um you can also do it at
+
+01:11:03.280 --> 01:11:10.560
+each decoder
+
+01:11:04.719 --> 01:11:11.640
+State and this is uh basically you can
+
+01:11:10.560 --> 01:11:13.040
+do this anytime you're doing
+
+01:11:11.640 --> 01:11:15.199
+reinforcement learning by just training
+
+01:11:13.040 --> 01:11:18.199
+a regression model that does this for
+
+01:11:15.199 --> 01:11:19.679
+you based on the rewards you get the
+
+01:11:18.199 --> 01:11:21.040
+important thing is the Baseline is not
+
+01:11:19.679 --> 01:11:22.640
+allowed to use any of your actual
+
+01:11:21.040 --> 01:11:25.679
+predictions because once you start using
+
+01:11:22.640 --> 01:11:26.640
+the predictions then um your uh it's not
+
+01:11:25.679 --> 01:11:28.679
+a
+
+01:11:26.640 --> 01:11:30.840
+baseline another option which is
+
+01:11:28.679 --> 01:11:33.440
+relatively easy to implement but can
+
+01:11:30.840 --> 01:11:36.320
+still be effective is you calculate the
+
+01:11:33.440 --> 01:11:38.719
+mean of the rewards in a batch and so if
+
+01:11:36.320 --> 01:11:40.880
+you have a big batch of data and your
+
+01:11:38.719 --> 01:11:44.440
+average reward in the batch is like
+
+01:11:40.880 --> 01:11:46.480
+0.4 uh then you just subtract that 0.4
+
+01:11:44.440 --> 01:11:50.080
+uh and calculate your reward based on
+
+01:11:46.480 --> 01:11:50.080
+that so that's another option that can
+
+01:11:51.800 --> 01:11:57.800
+use
+
+01:11:53.639 --> 01:12:00.000
+um a kind of extreme example of this uh
+
+01:11:57.800 --> 01:12:01.199
+of creating a baseline is contrasting
+
+01:12:00.000 --> 01:12:03.639
+pairwise
+
+01:12:01.199 --> 01:12:05.880
+examples um or
+
+01:12:03.639 --> 01:12:08.280
+contrasting different outputs for the
+
+01:12:05.880 --> 01:12:12.040
+same input
+
+01:12:08.280 --> 01:12:13.920
+and you can easily learn uh directly
+
+01:12:12.040 --> 01:12:16.239
+from pairwise Human
+
+01:12:13.920 --> 01:12:18.199
+preferences uh which can provide more
+
+01:12:16.239 --> 01:12:20.760
+stability because you know one is better
+
+01:12:18.199 --> 01:12:23.880
+than the other so you essentially can be
+
+01:12:20.760 --> 01:12:26.199
+sure that uh you're upweighting a better
+
+01:12:23.880 --> 01:12:29.560
+one and down weting a worse one
+
+01:12:26.199 --> 01:12:31.400
+um this is the idea behind DPO which is
+
+01:12:29.560 --> 01:12:33.719
+a recently pretty popular model but
+
+01:12:31.400 --> 01:12:36.800
+there's also other previous methods that
+
+01:12:33.719 --> 01:12:40.199
+did similar things and the way DPO works
+
+01:12:36.800 --> 01:12:45.040
+is it basically calculates this ratio of
+
+01:12:40.199 --> 01:12:49.280
+uh the probability of the new uh the new
+
+01:12:45.040 --> 01:12:51.639
+model to the old model but it UPS this
+
+01:12:49.280 --> 01:12:53.639
+probability for a good output and it
+
+01:12:51.639 --> 01:12:56.280
+downweights this probability for a bad
+
+01:12:53.639 --> 01:12:57.679
+output and so
+
+01:12:56.280 --> 01:13:00.120
+here we have our better outputs over
+
+01:12:57.679 --> 01:13:02.040
+here here we have our worse outputs and
+
+01:13:00.120 --> 01:13:03.600
+you just it's basically learning to
+
+01:13:02.040 --> 01:13:05.639
+upate the probability and downweight
+
+01:13:03.600 --> 01:13:09.320
+probability
+
+01:13:05.639 --> 01:13:09.320
+accordingly so
+
+01:13:09.360 --> 01:13:15.040
+um you can notice that DPO is very
+
+01:13:12.280 --> 01:13:18.040
+similar to PO um and that it's learning
+
+01:13:15.040 --> 01:13:19.679
+uh it's using these ratios but the
+
+01:13:18.040 --> 01:13:21.520
+disadvantage of this is you obviously
+
+01:13:19.679 --> 01:13:23.120
+require pairwise judgments and you can't
+
+01:13:21.520 --> 01:13:26.120
+learn a model if you don't have these
+
+01:13:23.120 --> 01:13:28.080
+pawise judgments so
+
+01:13:26.120 --> 01:13:30.760
+the
+
+01:13:28.080 --> 01:13:33.159
+beta yeah so the beta term is is
+
+01:13:30.760 --> 01:13:35.840
+basically a normalization term it's a
+
+01:13:33.159 --> 01:13:39.960
+hyper parameter um
+
+01:13:35.840 --> 01:13:41.840
+for DPO sorry I read the paper right
+
+01:13:39.960 --> 01:13:43.639
+when it came out and I don't remember if
+
+01:13:41.840 --> 01:13:45.600
+it's a direct derivation from the K
+
+01:13:43.639 --> 01:13:47.960
+Divergence term or not but I think it
+
+01:13:45.600 --> 01:13:49.800
+might be um I'd have to go back and look
+
+01:13:47.960 --> 01:13:50.480
+at the look at the paper but basically
+
+01:13:49.800 --> 01:13:53.600
+the
+
+01:13:50.480 --> 01:13:56.760
+more the larger this is the larger
+
+01:13:53.600 --> 01:13:59.320
+gradient steps you'll be
+
+01:13:56.760 --> 01:14:00.639
+it also um like you'll notice there
+
+01:13:59.320 --> 01:14:03.400
+sorry I didn't mention this but you'll
+
+01:14:00.639 --> 01:14:06.120
+notice there's a sigmoid term here so
+
+01:14:03.400 --> 01:14:09.000
+the the
+
+01:14:06.120 --> 01:14:10.080
+beta the larger you increase the beta
+
+01:14:09.000 --> 01:14:13.239
+the
+
+01:14:10.080 --> 01:14:16.600
+more small differences in these
+
+01:14:13.239 --> 01:14:18.719
+values like it basically like stretches
+
+01:14:16.600 --> 01:14:22.280
+or shrinks the sigmoid with respect to
+
+01:14:18.719 --> 01:14:24.120
+how beak the it is so it will um it will
+
+01:14:22.280 --> 01:14:25.800
+affect how much like small differences
+
+01:14:24.120 --> 01:14:27.960
+in this will affect
+
+01:14:25.800 --> 01:14:30.120
+but I I think this was derived from the
+
+01:14:27.960 --> 01:14:31.760
+K regularization term that we had
+
+01:14:30.120 --> 01:14:34.400
+previously in
+
+01:14:31.760 --> 01:14:35.800
+um in this slide here but I have to go
+
+01:14:34.400 --> 01:14:40.520
+back and double check unless somebody
+
+01:14:35.800 --> 01:14:43.239
+knows it is okay good yeah
+
+01:14:40.520 --> 01:14:45.000
+so I don't want to say wrong things but
+
+01:14:43.239 --> 01:14:48.239
+I also don't want
+
+01:14:45.000 --> 01:14:50.920
+to okay cool um and so then increasing
+
+01:14:48.239 --> 01:14:55.080
+batch size
+
+01:14:50.920 --> 01:14:57.360
+um because each uh another thing is um
+
+01:14:55.080 --> 01:14:58.440
+kind of NE necessarily reinforcement
+
+01:14:57.360 --> 01:14:59.920
+learning is going to have higher
+
+01:14:58.440 --> 01:15:01.400
+variance and maximum likelihood
+
+01:14:59.920 --> 01:15:04.199
+estimation just because we're doing samp
+
+01:15:01.400 --> 01:15:07.840
+playing and other things like this and
+
+01:15:04.199 --> 01:15:09.440
+um so one very simple thing you can do
+
+01:15:07.840 --> 01:15:11.280
+is just increase the number of examples
+
+01:15:09.440 --> 01:15:13.679
+or rollouts that you do before an update
+
+01:15:11.280 --> 01:15:15.800
+to stabilize and so I I would definitely
+
+01:15:13.679 --> 01:15:17.480
+suggest that if you're seeing any
+
+01:15:15.800 --> 01:15:18.679
+stability after doing all of the tricks
+
+01:15:17.480 --> 01:15:20.400
+that I mentioned before that you
+
+01:15:18.679 --> 01:15:23.040
+increase your batch size and often that
+
+01:15:20.400 --> 01:15:25.480
+can just resolve your problems
+
+01:15:23.040 --> 01:15:28.760
+um another uh
+
+01:15:25.480 --> 01:15:30.560
+thing that people often do is um save
+
+01:15:28.760 --> 01:15:32.040
+many many previous rollouts because
+
+01:15:30.560 --> 01:15:34.199
+generally doing rollouts is more
+
+01:15:32.040 --> 01:15:37.840
+expensive doing rollouts and collecting
+
+01:15:34.199 --> 01:15:39.560
+rewards is more expensive and so um you
+
+01:15:37.840 --> 01:15:42.360
+can save the roll outs that you have
+
+01:15:39.560 --> 01:15:43.840
+done before and uh keep them around so
+
+01:15:42.360 --> 01:15:46.600
+you can update parameters with larger
+
+01:15:43.840 --> 01:15:50.800
+batches in a more efficient
+
+01:15:46.600 --> 01:15:53.120
+way cool so that's all I have uh I just
+
+01:15:50.800 --> 01:15:54.400
+realized we're exactly at time so uh I
+
+01:15:53.120 --> 01:15:56.440
+should finish up here but I'll be happy
+
+01:15:54.400 --> 01:15:59.440
+to take any
+
+01:15:56.440 --> 01:15:59.440
+for
+
+01:16:01.679 --> 01:16:04.679
+thanks