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+WEBVTT
+
+00:00:00.760 --> 00:00:07.240
+he everyone so I'd like to get
+
+00:00:03.279 --> 00:00:09.320
+started the first thing is that um I
+
+00:00:07.240 --> 00:00:11.160
+heard from the adws people that they
+
+00:00:09.320 --> 00:00:14.440
+started the
+
+00:00:11.160 --> 00:00:17.840
+process of
+
+00:00:14.440 --> 00:00:19.400
+getting things issued on the 26th which
+
+00:00:17.840 --> 00:00:21.480
+is three days ago so you should be
+
+00:00:19.400 --> 00:00:23.560
+getting it soon uh for reference I
+
+00:00:21.480 --> 00:00:25.599
+submitted the form about seven days
+
+00:00:23.560 --> 00:00:28.359
+before that so they're moving very
+
+00:00:25.599 --> 00:00:29.599
+slowly but I think you should have AWS
+
+00:00:28.359 --> 00:00:31.920
+credits by the end of the week if you
+
+00:00:29.599 --> 00:00:35.120
+need them to run uh GPU machines or
+
+00:00:31.920 --> 00:00:37.960
+stuff like that the moment you get AWS
+
+00:00:35.120 --> 00:00:39.960
+credits or maybe even before you get AWS
+
+00:00:37.960 --> 00:00:43.320
+credits I might suggest that you try to
+
+00:00:39.960 --> 00:00:46.760
+start uh a GPU machine like a P2 machine
+
+00:00:43.320 --> 00:00:49.160
+or something like that because um
+
+00:00:46.760 --> 00:00:51.760
+sometimes you need to file for a limit
+
+00:00:49.160 --> 00:00:53.640
+increase uh to get a P2 machine and that
+
+00:00:51.760 --> 00:00:55.879
+also takes a little bit of time so I I
+
+00:00:53.640 --> 00:00:59.160
+would suggest that you uh you take a
+
+00:00:55.879 --> 00:01:01.160
+look at doing that um so you go to like
+
+00:00:59.160 --> 00:01:02.800
+if you're using AWS if you're not using
+
+00:01:01.160 --> 00:01:05.119
+AWS it doesn't matter but if you're
+
+00:01:02.800 --> 00:01:08.119
+using AWS you can go to launch instance
+
+00:01:05.119 --> 00:01:11.520
+and try to launch a p2x large machine um
+
+00:01:08.119 --> 00:01:13.159
+or something like that so uh but yeah
+
+00:01:11.520 --> 00:01:14.920
+anyway hopefully that will be done soon
+
+00:01:13.159 --> 00:01:16.600
+I'm sorry about the delay on this they
+
+00:01:14.920 --> 00:01:21.400
+said it would take seven days and it's
+
+00:01:16.600 --> 00:01:24.280
+taken almost twice at now so um my
+
+00:01:21.400 --> 00:01:26.439
+apologies any other uh things before we
+
+00:01:24.280 --> 00:01:26.439
+get
+
+00:01:28.759 --> 00:01:34.520
+started um okay I I don't see any so
+
+00:01:31.920 --> 00:01:37.280
+I'll go ahead with this um I have
+
+00:01:34.520 --> 00:01:39.240
+slightly fewer slides today so I might
+
+00:01:37.280 --> 00:01:40.960
+go a little bit off the slides and talk
+
+00:01:39.240 --> 00:01:44.759
+about papers and stuff or we might
+
+00:01:40.960 --> 00:01:46.920
+finish early uh either way so um but
+
+00:01:44.759 --> 00:01:48.439
+what I would like to talk about is um
+
+00:01:46.920 --> 00:01:53.320
+combining multiple
+
+00:01:48.439 --> 00:01:55.479
+models and this is uh really important
+
+00:01:53.320 --> 00:01:57.520
+and useful if you want to get like an
+
+00:01:55.479 --> 00:02:00.719
+extra few points of
+
+00:01:57.520 --> 00:02:03.159
+accuracy uh for anything basically
+
+00:02:00.719 --> 00:02:04.039
+because it's a pretty reliable way to
+
+00:02:03.159 --> 00:02:06.960
+get
+
+00:02:04.039 --> 00:02:08.879
+improvements um and there's a a bunch of
+
+00:02:06.960 --> 00:02:11.239
+different kind of related but different
+
+00:02:08.879 --> 00:02:13.680
+topics that I'm going to talk about
+
+00:02:11.239 --> 00:02:15.519
+today but anyway the the basic
+
+00:02:13.680 --> 00:02:19.239
+background is that we have many models
+
+00:02:15.519 --> 00:02:22.920
+uh that exist and the reason why we have
+
+00:02:19.239 --> 00:02:25.840
+many models that exist is multiple fold
+
+00:02:22.920 --> 00:02:28.160
+number one we could have different model
+
+00:02:25.840 --> 00:02:30.080
+architectures um and we could also have
+
+00:02:28.160 --> 00:02:34.440
+different initializations of those model
+
+00:02:30.080 --> 00:02:37.879
+architectures so um normally you know if
+
+00:02:34.440 --> 00:02:40.319
+we do initialization we will initial
+
+00:02:37.879 --> 00:02:42.360
+initialize our model architecture like
+
+00:02:40.319 --> 00:02:44.680
+let's say we initialize a llama
+
+00:02:42.360 --> 00:02:45.920
+architecture uh we start out with random
+
+00:02:44.680 --> 00:02:49.319
+7B
+
+00:02:45.920 --> 00:02:52.879
+parameters and then we train and we get
+
+00:02:49.319 --> 00:02:53.840
+llama 7B for uh our pre-training or
+
+00:02:52.879 --> 00:02:57.280
+llama
+
+00:02:53.840 --> 00:02:58.599
+27b um we might initialize another model
+
+00:02:57.280 --> 00:03:00.599
+this could be you know the same
+
+00:02:58.599 --> 00:03:02.360
+architecture different architecture Ure
+
+00:03:00.599 --> 00:03:04.840
+train it on the same data or different
+
+00:03:02.360 --> 00:03:07.000
+data and get something like mistol
+
+00:03:04.840 --> 00:03:08.599
+mistol 7B in this case actually maybe
+
+00:03:07.000 --> 00:03:10.080
+these are I should have indicated that
+
+00:03:08.599 --> 00:03:11.680
+these are different architectures but
+
+00:03:10.080 --> 00:03:13.879
+you know we get a different pre-rain
+
+00:03:11.680 --> 00:03:15.599
+model and of course uh we could also
+
+00:03:13.879 --> 00:03:18.640
+make it bigger or smaller or whatever
+
+00:03:15.599 --> 00:03:21.720
+else and then we get llama 270b over
+
+00:03:18.640 --> 00:03:23.519
+here and then after we do that there's a
+
+00:03:21.720 --> 00:03:25.319
+lot of fine tuning that goes on
+
+00:03:23.519 --> 00:03:29.360
+according to different strategies so we
+
+00:03:25.319 --> 00:03:32.640
+have um you know llama 27b instruct uh
+
+00:03:29.360 --> 00:03:37.760
+vun 7B uh version
+
+00:03:32.640 --> 00:03:41.000
+1.5 um mistol 7B instruct uh news uh
+
+00:03:37.760 --> 00:03:45.239
+Hermes 2 mistal 7B or llama 270b
+
+00:03:41.000 --> 00:03:47.239
+instruct so we have um a variety of
+
+00:03:45.239 --> 00:03:49.400
+architectures a variety of random
+
+00:03:47.239 --> 00:03:51.480
+initializations of those architectures a
+
+00:03:49.400 --> 00:03:54.799
+variety of pre-train models due to
+
+00:03:51.480 --> 00:03:57.439
+pre-training data or base models and
+
+00:03:54.799 --> 00:03:58.920
+then a variety of fine dun models um and
+
+00:03:57.439 --> 00:04:01.120
+so we have this kind of like branching
+
+00:03:58.920 --> 00:04:02.959
+tree basically
+
+00:04:01.120 --> 00:04:04.319
+um the reason why this is important is
+
+00:04:02.959 --> 00:04:06.680
+because when we're combining multiple
+
+00:04:04.319 --> 00:04:08.400
+models together some of the methods are
+
+00:04:06.680 --> 00:04:09.959
+applicable to completely different
+
+00:04:08.400 --> 00:04:12.439
+models some of the methods are only
+
+00:04:09.959 --> 00:04:15.000
+applicable to models that share the same
+
+00:04:12.439 --> 00:04:16.720
+architecture and some of them are only
+
+00:04:15.000 --> 00:04:19.199
+applicable to models that share the same
+
+00:04:16.720 --> 00:04:20.959
+initialization and training trajectory
+
+00:04:19.199 --> 00:04:23.680
+and so I'll try to distinguish between
+
+00:04:20.959 --> 00:04:23.680
+those as we go
+
+00:04:24.040 --> 00:04:27.919
+forward
+
+00:04:25.560 --> 00:04:29.960
+cool so the first thing I I'll talk
+
+00:04:27.919 --> 00:04:32.600
+about is model ensembling and and
+
+00:04:29.960 --> 00:04:34.320
+ensembling is kind of the a very general
+
+00:04:32.600 --> 00:04:37.600
+technique that you can use in a lot of
+
+00:04:34.320 --> 00:04:39.360
+different uh ways but it has its
+
+00:04:37.600 --> 00:04:43.039
+disadvantages as
+
+00:04:39.360 --> 00:04:47.199
+well so basically embling is combining
+
+00:04:43.039 --> 00:04:50.320
+the predictions from multiple models
+
+00:04:47.199 --> 00:04:52.400
+and the easiest way to do this ignore
+
+00:04:50.320 --> 00:04:53.800
+the lstm here this is just any sequence
+
+00:04:52.400 --> 00:04:56.320
+modeling thing it's because the slides
+
+00:04:53.800 --> 00:05:00.120
+are old but like let's say this is a a
+
+00:04:56.320 --> 00:05:03.360
+Transformer it is calculating the
+
+00:05:00.120 --> 00:05:05.600
+current decoder State and you make a
+
+00:05:03.360 --> 00:05:07.600
+prediction um this is calculating a
+
+00:05:05.600 --> 00:05:09.199
+current decoder State and make uh
+
+00:05:07.600 --> 00:05:11.560
+current decoders sayate in making a
+
+00:05:09.199 --> 00:05:13.039
+prediction and based on some combination
+
+00:05:11.560 --> 00:05:17.120
+of the two predictions you decide what
+
+00:05:13.039 --> 00:05:17.120
+you actually want to Output at the next
+
+00:05:17.680 --> 00:05:23.840
+step so why would we want to do this um
+
+00:05:22.080 --> 00:05:25.880
+does anyone have any ideas why we want
+
+00:05:23.840 --> 00:05:28.639
+to use two models instead of using one
+
+00:05:25.880 --> 00:05:31.639
+model or just using the best
+
+00:05:28.639 --> 00:05:31.639
+model
+
+00:05:32.319 --> 00:05:36.440
+or maybe in what situations we would
+
+00:05:34.520 --> 00:05:39.440
+want to do
+
+00:05:36.440 --> 00:05:39.440
+this
+
+00:05:45.400 --> 00:05:50.319
+yeah and what what's the advantage of
+
+00:05:47.960 --> 00:05:50.319
+doing
+
+00:05:51.600 --> 00:05:57.000
+that yeah it reduces a bias kind kind of
+
+00:05:54.800 --> 00:05:57.000
+yeah
+
+00:05:58.639 --> 00:06:01.639
+sure
+
+00:06:28.560 --> 00:06:31.560
+m
+
+00:06:35.400 --> 00:06:40.360
+yeah so um I I'll repeat all of these I
+
+00:06:38.599 --> 00:06:43.960
+think all of these are correct so number
+
+00:06:40.360 --> 00:06:47.479
+one um it reduces the bias uh caused by
+
+00:06:43.960 --> 00:06:49.199
+a single model uh number two it was it's
+
+00:06:47.479 --> 00:06:52.199
+kind of like a beian perspective which
+
+00:06:49.199 --> 00:06:54.000
+I'll talk about in a second and then
+
+00:06:52.199 --> 00:06:56.039
+number three we have different models
+
+00:06:54.000 --> 00:06:58.520
+and models are better at some things and
+
+00:06:56.039 --> 00:07:00.400
+worse at other things
+
+00:06:58.520 --> 00:07:02.720
+um
+
+00:07:00.400 --> 00:07:05.960
+so talking about the better at some
+
+00:07:02.720 --> 00:07:08.319
+things and worse at other things um the
+
+00:07:05.960 --> 00:07:10.960
+basic idea behind embling is that the
+
+00:07:08.319 --> 00:07:14.240
+errors that model m models make tend to
+
+00:07:10.960 --> 00:07:15.840
+not be consistent it not tend to not be
+
+00:07:14.240 --> 00:07:21.520
+as consistent as when the model is
+
+00:07:15.840 --> 00:07:24.800
+getting it correct so we might have um
+
+00:07:21.520 --> 00:07:26.160
+we might have one model that says uh
+
+00:07:24.800 --> 00:07:28.199
+like let's say we just have really
+
+00:07:26.160 --> 00:07:30.680
+really bad models this is kind of a
+
+00:07:28.199 --> 00:07:31.720
+really um
+
+00:07:30.680 --> 00:07:35.960
+obvious
+
+00:07:31.720 --> 00:07:38.440
+example but we have like the dog the dog
+
+00:07:35.960 --> 00:07:42.639
+barks and then
+
+00:07:38.440 --> 00:07:46.039
+runs and then uh Dives or something like
+
+00:07:42.639 --> 00:07:49.000
+that and we have uh one one model that
+
+00:07:46.039 --> 00:07:50.560
+just had tons of stuff about diving in
+
+00:07:49.000 --> 00:07:52.120
+its training data another model that had
+
+00:07:50.560 --> 00:07:54.240
+tons of stuff about running in its
+
+00:07:52.120 --> 00:07:56.560
+training data or or marathons or
+
+00:07:54.240 --> 00:08:00.039
+something staining data so we'll get
+
+00:07:56.560 --> 00:08:01.800
+model one and model one we'll to give
+
+00:08:00.039 --> 00:08:06.240
+like a probability of like
+
+00:08:01.800 --> 00:08:08.280
+0.3 maybe 0.4 and
+
+00:08:06.240 --> 00:08:10.360
+0.05 and then we'll have another one
+
+00:08:08.280 --> 00:08:13.039
+over here that's like
+
+00:08:10.360 --> 00:08:17.319
+0.32
+
+00:08:13.039 --> 00:08:19.759
+0.41 and 0 sorry
+
+00:08:17.319 --> 00:08:23.039
+0.05 and
+
+00:08:19.759 --> 00:08:25.759
+0.41 or something like this and so when
+
+00:08:23.039 --> 00:08:27.639
+you average the two together you tend to
+
+00:08:25.759 --> 00:08:29.240
+get the right answer more often because
+
+00:08:27.639 --> 00:08:31.720
+kind of the mistakes that they make tend
+
+00:08:29.240 --> 00:08:33.479
+to less correlated than the probability
+
+00:08:31.720 --> 00:08:35.880
+of getting and of course it's not
+
+00:08:33.479 --> 00:08:38.200
+perfect because unbled models are not
+
+00:08:35.880 --> 00:08:39.880
+perfect but this is a a general tendency
+
+00:08:38.200 --> 00:08:42.240
+that we see a lot in
+
+00:08:39.880 --> 00:08:45.959
+models
+
+00:08:42.240 --> 00:08:47.720
+um and um it's because of this it kind
+
+00:08:45.959 --> 00:08:52.320
+of Smooths over the idiosyncrasies of
+
+00:08:47.720 --> 00:08:54.800
+the models you can even um gist Ensemble
+
+00:08:52.320 --> 00:08:57.519
+models from different checkpoints and
+
+00:08:54.800 --> 00:08:58.959
+that still gives you improvements and so
+
+00:08:57.519 --> 00:09:00.560
+when you Ensemble models from different
+
+00:08:58.959 --> 00:09:02.600
+checkpoints it's basically just what
+
+00:09:00.560 --> 00:09:05.920
+data did they see most recently and that
+
+00:09:02.600 --> 00:09:07.839
+also Smooths over you know uh the fact
+
+00:09:05.920 --> 00:09:10.600
+that like this model happened to see
+
+00:09:07.839 --> 00:09:13.000
+some data more recently and so it's less
+
+00:09:10.600 --> 00:09:16.120
+uh you know it's biased towards doing
+
+00:09:13.000 --> 00:09:18.440
+that so uh this is a a pretty effective
+
+00:09:16.120 --> 00:09:20.079
+method this is one of the few methods
+
+00:09:18.440 --> 00:09:21.959
+that I know is going to improve my
+
+00:09:20.079 --> 00:09:25.120
+accuracy almost every time like there's
+
+00:09:21.959 --> 00:09:27.880
+a bunch of methods that you can apply um
+
+00:09:25.120 --> 00:09:29.680
+and I ensembling it's very rare for me
+
+00:09:27.880 --> 00:09:31.959
+to Ensemble two models together not get
+
+00:09:29.680 --> 00:09:34.839
+a boost in accuracy in some way so it's
+
+00:09:31.959 --> 00:09:34.839
+a good thing to
+
+00:09:35.600 --> 00:09:41.040
+that there's two main ways to combine
+
+00:09:38.680 --> 00:09:42.560
+models together and both of them are
+
+00:09:41.040 --> 00:09:45.800
+useful in different
+
+00:09:42.560 --> 00:09:48.079
+situations the first one is linear
+
+00:09:45.800 --> 00:09:49.600
+interpolation and when you do linear
+
+00:09:48.079 --> 00:09:51.240
+interpolation basically what you're
+
+00:09:49.600 --> 00:09:53.720
+doing is you're taking the weighted
+
+00:09:51.240 --> 00:09:56.839
+average of model
+
+00:09:53.720 --> 00:10:00.360
+probabilities and the way that looks
+
+00:09:56.839 --> 00:10:04.040
+mathematically is like this um this is a
+
+00:10:00.360 --> 00:10:05.680
+probability according to the model M so
+
+00:10:04.040 --> 00:10:08.000
+this is just you know the probability of
+
+00:10:05.680 --> 00:10:11.720
+the next token according to model M this
+
+00:10:08.000 --> 00:10:13.200
+is the probability of selecting model M
+
+00:10:11.720 --> 00:10:18.040
+so you talked a little bit about the
+
+00:10:13.200 --> 00:10:19.920
+basian approach uh to this and this is
+
+00:10:18.040 --> 00:10:23.519
+basically saying what is the probability
+
+00:10:19.920 --> 00:10:26.519
+that the parameters of model M
+
+00:10:23.519 --> 00:10:30.320
+are the ones that we want to be choosing
+
+00:10:26.519 --> 00:10:32.680
+in this at this particular time step and
+
+00:10:30.320 --> 00:10:34.640
+then we will we will calculate this and
+
+00:10:32.680 --> 00:10:38.120
+so then you take the sum over this and
+
+00:10:34.640 --> 00:10:38.120
+this gives you the next
+
+00:10:39.560 --> 00:10:44.800
+probability for the second term you can
+
+00:10:42.639 --> 00:10:47.120
+do this in two ways the most common way
+
+00:10:44.800 --> 00:10:51.800
+to do this is just to have this be a
+
+00:10:47.120 --> 00:10:55.279
+constant so you you basically
+
+00:10:51.800 --> 00:10:55.279
+Define mixture
+
+00:10:55.920 --> 00:11:01.240
+weights uh which are like um
+
+00:11:08.480 --> 00:11:13.480
+where the sum of the mixture weights is
+
+00:11:10.760 --> 00:11:16.160
+equal to one and this is always between
+
+00:11:13.480 --> 00:11:18.639
+zero and one and so if you do this then
+
+00:11:16.160 --> 00:11:21.000
+this is just constant and you can uh
+
+00:11:18.639 --> 00:11:23.519
+interpolate them together constantly but
+
+00:11:21.000 --> 00:11:25.680
+you can also actually explicitly model
+
+00:11:23.519 --> 00:11:27.240
+this probability and say oh I'm
+
+00:11:25.680 --> 00:11:30.279
+currently in a situation where I really
+
+00:11:27.240 --> 00:11:31.880
+think model M will do a good job of uh
+
+00:11:30.279 --> 00:11:33.440
+you know predicting the probability so I
+
+00:11:31.880 --> 00:11:36.160
+want to put most of my probability on
+
+00:11:33.440 --> 00:11:39.000
+model M so you can actually learn this
+
+00:11:36.160 --> 00:11:40.079
+dynamically as well um and so if you
+
+00:11:39.000 --> 00:11:44.360
+have
+
+00:11:40.079 --> 00:11:45.920
+uh this actually um is rather practical
+
+00:11:44.360 --> 00:11:47.120
+and easy to do because what you can do
+
+00:11:45.920 --> 00:11:48.920
+is you can just calculate the
+
+00:11:47.120 --> 00:11:51.399
+probability according to each model at
+
+00:11:48.920 --> 00:11:53.120
+each time step and train this model
+
+00:11:51.399 --> 00:11:55.519
+separately without loading these models
+
+00:11:53.120 --> 00:11:59.399
+into memory at at the time of training
+
+00:11:55.519 --> 00:12:00.959
+those models so uh yeah this is um some
+
+00:11:59.399 --> 00:12:04.800
+you can do as
+
+00:12:00.959 --> 00:12:04.800
+well any questions about
+
+00:12:06.680 --> 00:12:11.920
+this
+
+00:12:08.519 --> 00:12:14.000
+Okay cool so the other option is log
+
+00:12:11.920 --> 00:12:15.800
+linear interpolation and so linear
+
+00:12:14.000 --> 00:12:18.680
+interpolation you're taking a linear
+
+00:12:15.800 --> 00:12:22.040
+combination of the probabilities of each
+
+00:12:18.680 --> 00:12:24.959
+model log linear interpolation you're
+
+00:12:22.040 --> 00:12:26.079
+combining together the log probabilities
+
+00:12:24.959 --> 00:12:29.519
+of each
+
+00:12:26.079 --> 00:12:32.639
+model and then renormalizing so so that
+
+00:12:29.519 --> 00:12:34.920
+you get um that you get an actual
+
+00:12:32.639 --> 00:12:37.760
+probabilistic output so basically what
+
+00:12:34.920 --> 00:12:40.720
+you do is you have this uh interpolation
+
+00:12:37.760 --> 00:12:44.040
+coefficient like I had before but you're
+
+00:12:40.720 --> 00:12:44.040
+combining together the log
+
+00:12:44.639 --> 00:12:49.639
+probabilities and so here we need to
+
+00:12:47.680 --> 00:12:51.320
+take the soft
+
+00:12:49.639 --> 00:12:53.760
+Max
+
+00:12:51.320 --> 00:12:55.760
+um thinking back here I didn't take the
+
+00:12:53.760 --> 00:12:58.120
+softmax does anyone have an idea why I
+
+00:12:55.760 --> 00:13:02.000
+didn't take the soft
+
+00:12:58.120 --> 00:13:02.000
+Max or why I didn't need
+
+00:13:08.160 --> 00:13:12.199
+to why why I need to
+
+00:13:21.600 --> 00:13:27.680
+here yeah
+
+00:13:23.639 --> 00:13:30.440
+so this probability is gu to be z z and
+
+00:13:27.680 --> 00:13:32.240
+one and add up to one this probability
+
+00:13:30.440 --> 00:13:33.760
+is also guaranteed to be zero and one
+
+00:13:32.240 --> 00:13:35.680
+and add up to one and then when you
+
+00:13:33.760 --> 00:13:37.120
+multiply those together uh you can do a
+
+00:13:35.680 --> 00:13:39.160
+little bit of math and demonstrate that
+
+00:13:37.120 --> 00:13:41.440
+the resulting thing will be between zero
+
+00:13:39.160 --> 00:13:42.839
+and one and add up to one that's not the
+
+00:13:41.440 --> 00:13:44.399
+case anymore when we start doing things
+
+00:13:42.839 --> 00:13:47.639
+in log space because it's just not a
+
+00:13:44.399 --> 00:13:50.160
+linear function anyway so um you need to
+
+00:13:47.639 --> 00:13:51.959
+renormalize like this luckily this is
+
+00:13:50.160 --> 00:13:54.920
+super easy like anything else you do in
+
+00:13:51.959 --> 00:13:56.959
+py torch you just add things together
+
+00:13:54.920 --> 00:13:59.320
+and take a soft Max and you'll you'll
+
+00:13:56.959 --> 00:14:02.519
+get an output but you do need to do
+
+00:13:59.320 --> 00:14:05.279
+otherwise you're going to get something
+
+00:14:02.519 --> 00:14:07.279
+weird um the interpolation coefficient
+
+00:14:05.279 --> 00:14:09.639
+here also can be set to a constant so
+
+00:14:07.279 --> 00:14:12.759
+you can you could learn it uh kind of
+
+00:14:09.639 --> 00:14:15.320
+dynamically or it could be
+
+00:14:12.759 --> 00:14:17.720
+separate cool and these actually have
+
+00:14:15.320 --> 00:14:19.639
+different meaning oh sorry go ahead you
+
+00:14:17.720 --> 00:14:23.880
+T on
+
+00:14:19.639 --> 00:14:26.759
+the Yeah Yeah so basically the
+
+00:14:23.880 --> 00:14:29.880
+way the way you would do this is you
+
+00:14:26.759 --> 00:14:32.399
+would have either
+
+00:14:29.880 --> 00:14:33.920
+the same model you you would either take
+
+00:14:32.399 --> 00:14:35.279
+representations from one of these
+
+00:14:33.920 --> 00:14:37.480
+language models or you would take
+
+00:14:35.279 --> 00:14:38.440
+representations from another model and
+
+00:14:37.480 --> 00:14:41.639
+you would
+
+00:14:38.440 --> 00:14:43.959
+just have a model that
+
+00:14:41.639 --> 00:14:46.480
+predicts uh what this interpolation
+
+00:14:43.959 --> 00:14:48.279
+coefficient would be and the
+
+00:14:46.480 --> 00:14:49.720
+optimization objective for that
+
+00:14:48.279 --> 00:14:52.759
+interpolation coefficient is just
+
+00:14:49.720 --> 00:14:56.120
+maximizing the probability
+
+00:14:52.759 --> 00:14:59.600
+whatever so this could also be good um
+
+00:14:56.120 --> 00:15:01.839
+because this interpolation coefficient
+
+00:14:59.600 --> 00:15:07.160
+only like let's say you're interpolating
+
+00:15:01.839 --> 00:15:09.399
+two models together it has one degree of
+
+00:15:07.160 --> 00:15:13.320
+Freedom at each time step right because
+
+00:15:09.399 --> 00:15:15.320
+you're only predicting a probability um
+
+00:15:13.320 --> 00:15:17.839
+if you have uh if you have five models
+
+00:15:15.320 --> 00:15:20.240
+you have uh you basically would be doing
+
+00:15:17.839 --> 00:15:24.199
+a soft match over
+
+00:15:20.240 --> 00:15:25.519
+five five outputs and that's a lot fewer
+
+00:15:24.199 --> 00:15:27.600
+that's a lot fewer than the whole
+
+00:15:25.519 --> 00:15:29.880
+vocabulary right and so this is
+
+00:15:27.600 --> 00:15:31.639
+relatively learning a good interpolation
+
+00:15:29.880 --> 00:15:34.160
+coefficient is relatively easy compared
+
+00:15:31.639 --> 00:15:35.800
+to learning what word to predict next
+
+00:15:34.160 --> 00:15:36.880
+and because of this you could actually
+
+00:15:35.800 --> 00:15:39.759
+tune
+
+00:15:36.880 --> 00:15:42.880
+this um sorry you could tune this
+
+00:15:39.759 --> 00:15:44.600
+probability on a very small data set and
+
+00:15:42.880 --> 00:15:46.959
+you could even have it be context
+
+00:15:44.600 --> 00:15:48.480
+independent so you could just be you
+
+00:15:46.959 --> 00:15:51.399
+know
+
+00:15:48.480 --> 00:15:55.880
+calculating literally five five
+
+00:15:51.399 --> 00:15:57.399
+parameters here um and so because of
+
+00:15:55.880 --> 00:16:00.319
+that like let's say you have a special
+
+00:15:57.399 --> 00:16:02.639
+domain or a special task where you have
+
+00:16:00.319 --> 00:16:04.920
+like 50 training examples or something
+
+00:16:02.639 --> 00:16:07.399
+like that or you know 100 training
+
+00:16:04.920 --> 00:16:08.959
+examples you can learn this
+
+00:16:07.399 --> 00:16:12.480
+interpolation coefficient very
+
+00:16:08.959 --> 00:16:15.880
+effectively uh on just a few a very
+
+00:16:12.480 --> 00:16:18.120
+small number of training examples um but
+
+00:16:15.880 --> 00:16:20.000
+like it could be very useful because
+
+00:16:18.120 --> 00:16:23.920
+like let's say you have a special domain
+
+00:16:20.000 --> 00:16:25.639
+medical language model that's 1.3
+
+00:16:23.920 --> 00:16:27.759
+billion parameters that you trained
+
+00:16:25.639 --> 00:16:29.639
+yourself and then you have a 70 billion
+
+00:16:27.759 --> 00:16:31.079
+parameter language model
+
+00:16:29.639 --> 00:16:33.680
+that's like really good at modeling
+
+00:16:31.079 --> 00:16:35.399
+General English um so then you could
+
+00:16:33.680 --> 00:16:39.120
+learn the interpolation coefficient
+
+00:16:35.399 --> 00:16:40.600
+between those two such that um the large
+
+00:16:39.120 --> 00:16:41.800
+general purpose language model will be
+
+00:16:40.600 --> 00:16:43.959
+generating all of the kind of
+
+00:16:41.800 --> 00:16:46.360
+grammatical stuff but whenever you
+
+00:16:43.959 --> 00:16:48.480
+switch over to modeling technical terms
+
+00:16:46.360 --> 00:16:50.040
+from the medical domain then it learns
+
+00:16:48.480 --> 00:16:52.480
+to upweight the medical language model
+
+00:16:50.040 --> 00:16:54.199
+or something so this can be quite uh
+
+00:16:52.480 --> 00:16:57.000
+this can be quite effective if you have
+
+00:16:54.199 --> 00:17:00.839
+a limited amount of data that you want
+
+00:16:57.000 --> 00:17:00.839
+toing thiss
+
+00:17:01.240 --> 00:17:05.600
+um any other questions about that
+
+00:17:09.079 --> 00:17:14.880
+yeah yeah I'm just gonna talk about that
+
+00:17:11.760 --> 00:17:17.640
+next so linear versus log linear you can
+
+00:17:14.880 --> 00:17:20.880
+actually think of this in logic um and
+
+00:17:17.640 --> 00:17:23.640
+what I mean by that is um linear is kind
+
+00:17:20.880 --> 00:17:26.640
+of like a logical or it tries to come up
+
+00:17:23.640 --> 00:17:29.600
+with examples where either one of the
+
+00:17:26.640 --> 00:17:31.679
+two assigns a high probability so we
+
+00:17:29.600 --> 00:17:36.200
+have the example of like bark
+
+00:17:31.679 --> 00:17:36.200
+run um bark run
+
+00:17:55.640 --> 00:18:03.840
+diet so if we take the average of these
+
+00:18:00.360 --> 00:18:03.840
+two in linear
+
+00:18:04.120 --> 00:18:10.240
+space this would be
+
+00:18:07.159 --> 00:18:13.679
+0.2 this would be
+
+00:18:10.240 --> 00:18:17.240
+0.26 and this would
+
+00:18:13.679 --> 00:18:17.240
+be um
+
+00:18:17.400 --> 00:18:26.280
+0.21 and so a a linear combination
+
+00:18:21.480 --> 00:18:28.600
+between the two will find run to be the
+
+00:18:26.280 --> 00:18:30.600
+highest scoring one because on the left
+
+00:18:28.600 --> 00:18:32.280
+side we have one model that really likes
+
+00:18:30.600 --> 00:18:33.159
+this output and we have another model
+
+00:18:32.280 --> 00:18:35.159
+that
+
+00:18:33.159 --> 00:18:39.280
+doesn't
+
+00:18:35.159 --> 00:18:42.159
+um this is this can be good at using
+
+00:18:39.280 --> 00:18:44.440
+models that capture uh different traits
+
+00:18:42.159 --> 00:18:47.679
+or it can also be useful if like for
+
+00:18:44.440 --> 00:18:49.840
+example you have a you have a small
+
+00:18:47.679 --> 00:18:52.320
+model that you really that really
+
+00:18:49.840 --> 00:18:53.840
+captures like very specific vocabulary
+
+00:18:52.320 --> 00:18:55.520
+and you want to upgrate that specific
+
+00:18:53.840 --> 00:18:56.799
+vocabulary that gets a really low
+
+00:18:55.520 --> 00:18:57.720
+probability according to a general
+
+00:18:56.799 --> 00:19:01.360
+purpose
+
+00:18:57.720 --> 00:19:03.200
+model um this is also necessary when any
+
+00:19:01.360 --> 00:19:04.520
+model can assign zero probabilities so
+
+00:19:03.200 --> 00:19:06.720
+if you have like an example of
+
+00:19:04.520 --> 00:19:10.080
+vocabulary that isn't included in the
+
+00:19:06.720 --> 00:19:11.159
+the like vocabulary of another model or
+
+00:19:10.080 --> 00:19:14.280
+you have models with different
+
+00:19:11.159 --> 00:19:17.200
+vocabularies it's necessary to do this
+
+00:19:14.280 --> 00:19:19.200
+log linear is more like logical and um
+
+00:19:17.200 --> 00:19:22.240
+so the interpolated model only likes
+
+00:19:19.200 --> 00:19:23.799
+choices where all the models agree and
+
+00:19:22.240 --> 00:19:25.640
+this is particularly good when you want
+
+00:19:23.799 --> 00:19:27.440
+to restrict possible answers like you
+
+00:19:25.640 --> 00:19:29.280
+want to have one model be able to say no
+
+00:19:27.440 --> 00:19:32.080
+I really don't like this so never output
+
+00:19:29.280 --> 00:19:34.200
+it so um for example if you wanted to
+
+00:19:32.080 --> 00:19:37.360
+train a model that you knew was very
+
+00:19:34.200 --> 00:19:38.919
+adverse to toxic language and prevent uh
+
+00:19:37.360 --> 00:19:42.600
+the model from outputting toxic language
+
+00:19:38.919 --> 00:19:45.200
+you could use log linear mod so I I
+
+00:19:42.600 --> 00:19:47.559
+can't unfortunately uh calculate logs
+
+00:19:45.200 --> 00:19:50.080
+and exponents in my head well enough to
+
+00:19:47.559 --> 00:19:51.600
+uh to decide this but I'm sure that a
+
+00:19:50.080 --> 00:19:53.840
+linear
+
+00:19:51.600 --> 00:19:56.840
+model the linear model would pick the
+
+00:19:53.840 --> 00:19:59.600
+first one here and the log linear
+
+00:19:56.840 --> 00:20:01.679
+model would pick the second one because
+
+00:19:59.600 --> 00:20:05.640
+the second one has a very low score here
+
+00:20:01.679 --> 00:20:08.640
+so that would be downrated um
+
+00:20:05.640 --> 00:20:08.640
+by
+
+00:20:16.919 --> 00:20:20.640
+yeah yeah so
+
+00:20:25.840 --> 00:20:31.000
+if yeah and if there's any chance of
+
+00:20:28.760 --> 00:20:34.159
+assigning zero probability according to
+
+00:20:31.000 --> 00:20:36.520
+a language model then really you can't
+
+00:20:34.159 --> 00:20:38.200
+even test that language model on that on
+
+00:20:36.520 --> 00:20:42.120
+that test set
+
+00:20:38.200 --> 00:20:43.640
+um so the issue becomes like let's say
+
+00:20:42.120 --> 00:20:45.559
+you have two models with different
+
+00:20:43.640 --> 00:20:47.080
+vocabulary if you have two models with
+
+00:20:45.559 --> 00:20:49.080
+different vocabulary it becomes very
+
+00:20:47.080 --> 00:20:50.559
+tricky how to reconcile those two but
+
+00:20:49.080 --> 00:20:53.440
+you could do linear interpolation
+
+00:20:50.559 --> 00:20:55.200
+between them like match the vocab the
+
+00:20:53.440 --> 00:20:57.559
+output vocabularies that they do have
+
+00:20:55.200 --> 00:21:00.120
+and then just not worry about the fact
+
+00:20:57.559 --> 00:21:02.760
+that the vocabularies are dis jointed
+
+00:21:00.120 --> 00:21:05.039
+and because one will assign a zero
+
+00:21:02.760 --> 00:21:07.280
+probability to those vocabulary items
+
+00:21:05.039 --> 00:21:12.240
+but the other one is fine so you can
+
+00:21:07.280 --> 00:21:14.919
+just do that but if you're in general it
+
+00:21:12.240 --> 00:21:16.480
+will be very tricky to try to get two
+
+00:21:14.919 --> 00:21:18.559
+models with different vocabularies to
+
+00:21:16.480 --> 00:21:21.480
+play together nicely so I I would
+
+00:21:18.559 --> 00:21:22.919
+suggest um thinking about thinking
+
+00:21:21.480 --> 00:21:25.600
+seriously about whether you need to do
+
+00:21:22.919 --> 00:21:31.360
+that or not before you start out but
+
+00:21:25.600 --> 00:21:31.360
+yeah um uh yes there any
+
+00:21:35.559 --> 00:21:40.960
+other
+
+00:21:38.039 --> 00:21:43.360
+um you could definitely so the question
+
+00:21:40.960 --> 00:21:45.000
+is are there any other types of
+
+00:21:43.360 --> 00:21:47.760
+interpolation that have other types of
+
+00:21:45.000 --> 00:21:50.159
+logical components like exor or nor um
+
+00:21:47.760 --> 00:21:52.840
+you could definitely come up with one uh
+
+00:21:50.159 --> 00:21:55.440
+I I am struggling a little bit to think
+
+00:21:52.840 --> 00:21:57.520
+about when you would want to do that but
+
+00:21:55.440 --> 00:22:02.840
+I'm sure
+
+00:21:57.520 --> 00:22:05.840
+you is is the inherent that the
+
+00:22:02.840 --> 00:22:05.840
+err
+
+00:22:09.120 --> 00:22:14.480
+not so what what if the errors are not
+
+00:22:12.640 --> 00:22:15.919
+what if the errors are correlated so
+
+00:22:14.480 --> 00:22:18.200
+think about what happens if the errors
+
+00:22:15.919 --> 00:22:20.000
+are perfectly correlated um which is
+
+00:22:18.200 --> 00:22:25.840
+when you're using the same model in two
+
+00:22:20.000 --> 00:22:25.840
+parts of the uh like on top so you
+
+00:22:27.000 --> 00:22:30.520
+literally uh these
+
+00:22:29.159 --> 00:22:32.679
+model one and model two are the same
+
+00:22:30.520 --> 00:22:36.720
+model if that's the case nothing happens
+
+00:22:32.679 --> 00:22:39.200
+it doesn't get worse um and
+
+00:22:36.720 --> 00:22:43.039
+so of course because this is machine
+
+00:22:39.200 --> 00:22:45.080
+learning there's no guarantee like you
+
+00:22:43.039 --> 00:22:47.559
+know unless we make some assumptions
+
+00:22:45.080 --> 00:22:49.200
+about the relationship between like the
+
+00:22:47.559 --> 00:22:52.279
+training set and the test set or the
+
+00:22:49.200 --> 00:22:53.760
+models errors in the test set um you can
+
+00:22:52.279 --> 00:22:57.039
+always do something that will make your
+
+00:22:53.760 --> 00:22:59.240
+accuracy worse um like let's say we flip
+
+00:22:57.039 --> 00:23:00.360
+the labels of a binary class
+
+00:22:59.240 --> 00:23:03.120
+no matter what you do you're going to
+
+00:23:00.360 --> 00:23:06.320
+make your accuracy worse but
+
+00:23:03.120 --> 00:23:09.000
+um no matter what the normal thing you
+
+00:23:06.320 --> 00:23:10.640
+would do is it would make your if it
+
+00:23:09.000 --> 00:23:12.480
+would improve accuracy normally it would
+
+00:23:10.640 --> 00:23:14.760
+decrease your accuracy but like under
+
+00:23:12.480 --> 00:23:16.080
+pretty reasonable assumptions it's
+
+00:23:14.760 --> 00:23:20.400
+mostly going to be the case that errors
+
+00:23:16.080 --> 00:23:22.320
+are deated to some extent um
+
+00:23:20.400 --> 00:23:25.559
+so
+
+00:23:22.320 --> 00:23:30.440
+yeah you and because of that ensembly
+
+00:23:25.559 --> 00:23:30.440
+usually helps yeah
+
+00:23:36.120 --> 00:23:42.019
+um about which one
+
+00:23:38.760 --> 00:23:42.019
+[Music]
+
+00:23:53.559 --> 00:24:01.240
+which let me make sure I didn't mess it
+
+00:23:55.640 --> 00:24:01.240
+up on sides okay so in my
+
+00:24:06.960 --> 00:24:13.120
+example yeah yeah
+
+00:24:09.640 --> 00:24:13.120
+yeah sorry about
+
+00:24:14.360 --> 00:24:19.320
+that because this is this is where the
+
+00:24:17.039 --> 00:24:21.840
+average is higher and then this is
+
+00:24:19.320 --> 00:24:27.200
+one take
+
+00:24:21.840 --> 00:24:29.039
+you uh cool any other any other
+
+00:24:27.200 --> 00:24:31.840
+questions okay
+
+00:24:29.039 --> 00:24:34.440
+okay so
+
+00:24:31.840 --> 00:24:36.320
+um another thing I should point out is
+
+00:24:34.440 --> 00:24:39.600
+that we don't
+
+00:24:36.320 --> 00:24:41.840
+necessarily need to use models only as
+
+00:24:39.600 --> 00:24:44.080
+positive evidence so if you're using log
+
+00:24:41.840 --> 00:24:46.039
+linear interpolation actually your
+
+00:24:44.080 --> 00:24:49.919
+interpolation coefficients do not need
+
+00:24:46.039 --> 00:24:52.520
+to be positive they can also be negative
+
+00:24:49.919 --> 00:24:55.360
+and you can have uh things where you
+
+00:24:52.520 --> 00:24:57.840
+penalize the probabilities given by a
+
+00:24:55.360 --> 00:24:59.679
+particular model and this has actually
+
+00:24:57.840 --> 00:25:01.520
+been used for a long time it was
+
+00:24:59.679 --> 00:25:04.440
+actually used in machine translation
+
+00:25:01.520 --> 00:25:08.840
+since like uh 2005 or something like
+
+00:25:04.440 --> 00:25:11.480
+this but the basic idea is um that you
+
+00:25:08.840 --> 00:25:13.600
+have some models that serve as negative
+
+00:25:11.480 --> 00:25:15.559
+evidence so you have kind of a core
+
+00:25:13.600 --> 00:25:17.880
+model this might be your really strong
+
+00:25:15.559 --> 00:25:21.520
+general purpose language model you have
+
+00:25:17.880 --> 00:25:23.080
+a positive uh model which is the model
+
+00:25:21.520 --> 00:25:25.240
+that you want to kind of boost up and
+
+00:25:23.080 --> 00:25:27.320
+improve and a negative model which you
+
+00:25:25.240 --> 00:25:31.159
+want to
+
+00:25:27.320 --> 00:25:33.679
+decrease and um one example of this is
+
+00:25:31.159 --> 00:25:36.760
+in uh a paper that we did in
+
+00:25:33.679 --> 00:25:40.159
+2019 um the core was a machine
+
+00:25:36.760 --> 00:25:42.960
+translation model and the negative model
+
+00:25:40.159 --> 00:25:44.880
+is an outof domain language model and
+
+00:25:42.960 --> 00:25:46.960
+the positive model is an in domain
+
+00:25:44.880 --> 00:25:51.039
+language model and so the idea behind
+
+00:25:46.960 --> 00:25:53.880
+this is a machine translation model um
+
+00:25:51.039 --> 00:25:55.600
+you have to train it on machine
+
+00:25:53.880 --> 00:25:58.320
+translation data and machine translation
+
+00:25:55.600 --> 00:26:00.640
+data is not very easy to get for
+
+00:25:58.320 --> 00:26:02.360
+particular domains for example um you
+
+00:26:00.640 --> 00:26:03.880
+might only have machine translation data
+
+00:26:02.360 --> 00:26:06.919
+in the news domain and you actually want
+
+00:26:03.880 --> 00:26:09.240
+to be uh doing uh translation in the
+
+00:26:06.919 --> 00:26:12.720
+medical domain or something so what you
+
+00:26:09.240 --> 00:26:14.640
+do is you have your positive model here
+
+00:26:12.720 --> 00:26:17.600
+this could be a new this is a machine
+
+00:26:14.640 --> 00:26:19.919
+translation model this could be a news
+
+00:26:17.600 --> 00:26:21.320
+domain or sorry this could be a medical
+
+00:26:19.919 --> 00:26:22.919
+domain language model and this could be
+
+00:26:21.320 --> 00:26:24.360
+a news domain language model so you're
+
+00:26:22.919 --> 00:26:25.840
+subtracting out the news domain
+
+00:26:24.360 --> 00:26:27.600
+probabilities and adding in medical
+
+00:26:25.840 --> 00:26:30.240
+domain probabilities move it in that
+
+00:26:27.600 --> 00:26:30.240
+direction
+
+00:26:30.440 --> 00:26:36.799
+um another example of this is uh
+
+00:26:32.919 --> 00:26:40.000
+something called uh D experts um or
+
+00:26:36.799 --> 00:26:43.440
+dexperts and the idea here is here you
+
+00:26:40.000 --> 00:26:46.120
+have a strong language model as your
+
+00:26:43.440 --> 00:26:48.320
+core and then as negative you have a
+
+00:26:46.120 --> 00:26:50.240
+weak toxic language model so it was
+
+00:26:48.320 --> 00:26:52.760
+trained on lot lots of like bad texts
+
+00:26:50.240 --> 00:26:55.799
+that you don't want to be generating and
+
+00:26:52.760 --> 00:26:57.159
+the positive is a weak non-toxic
+
+00:26:55.799 --> 00:26:59.279
+language model that was trained on lots
+
+00:26:57.159 --> 00:27:03.200
+of like inocua
+
+00:26:59.279 --> 00:27:04.399
+posts so that would help you detoxify
+
+00:27:03.200 --> 00:27:06.679
+the outputs of the
+
+00:27:04.399 --> 00:27:09.799
+language so there's lots of examples of
+
+00:27:06.679 --> 00:27:09.799
+things like this that you can do
+
+00:27:10.720 --> 00:27:15.880
+through
+
+00:27:12.880 --> 00:27:15.880
+yeah
+
+00:27:19.320 --> 00:27:25.880
+yeah um so the positive in the machine
+
+00:27:22.840 --> 00:27:27.679
+translation example this is a so this is
+
+00:27:25.880 --> 00:27:31.760
+a machine translation model where the
+
+00:27:27.679 --> 00:27:34.080
+input is is like in um English and out
+
+00:27:31.760 --> 00:27:37.880
+is in Japanese something like
+
+00:27:34.080 --> 00:27:39.679
+that this is only trained on Japanese
+
+00:27:37.880 --> 00:27:42.919
+but it's trained on like medical
+
+00:27:39.679 --> 00:27:44.440
+Japanese for example Med the domain one
+
+00:27:42.919 --> 00:27:48.480
+this is a language model that was
+
+00:27:44.440 --> 00:27:50.600
+trained on like news domain um Japanese
+
+00:27:48.480 --> 00:27:54.039
+or it could even literally just be
+
+00:27:50.600 --> 00:27:56.360
+trained on the side of the machine
+
+00:27:54.039 --> 00:28:00.120
+trans um so it's trying to remove out
+
+00:27:56.360 --> 00:28:00.120
+the language modeling component from the
+
+00:28:03.720 --> 00:28:06.720
+cool
+
+00:28:06.880 --> 00:28:11.480
+okay so another thing that I should
+
+00:28:09.880 --> 00:28:14.720
+point out I didn't actually put it on
+
+00:28:11.480 --> 00:28:18.399
+the slides is um there's a lot of other
+
+00:28:14.720 --> 00:28:19.640
+ways to get multiple models and um I
+
+00:28:18.399 --> 00:28:22.600
+think a lot of people are probably
+
+00:28:19.640 --> 00:28:23.559
+familiar with Dropout um it's a method
+
+00:28:22.600 --> 00:28:27.120
+for
+
+00:28:23.559 --> 00:28:29.080
+regularizing um it's a method for
+
+00:28:27.120 --> 00:28:31.120
+regularizing
+
+00:28:29.080 --> 00:28:33.760
+neural networks or deep learning models
+
+00:28:31.120 --> 00:28:37.279
+in general and basically the idea is
+
+00:28:33.760 --> 00:28:41.840
+every once in a while um during training
+
+00:28:37.279 --> 00:28:45.720
+you drop out some portion of the uh like
+
+00:28:41.840 --> 00:28:48.919
+nodes in the neural network model and
+
+00:28:45.720 --> 00:28:51.320
+you can actually drop
+
+00:28:48.919 --> 00:28:52.640
+out and normally what you do is at test
+
+00:28:51.320 --> 00:28:53.919
+time then you just don't drop out
+
+00:28:52.640 --> 00:28:56.039
+anything and you use the whole neural
+
+00:28:53.919 --> 00:28:59.960
+network model but another thing you can
+
+00:28:56.039 --> 00:29:02.559
+do is you can drop out a test time drop
+
+00:28:59.960 --> 00:29:04.679
+out five times and combine those
+
+00:29:02.559 --> 00:29:06.600
+different models together through ensom
+
+00:29:04.679 --> 00:29:10.600
+and that's actually something uh that
+
+00:29:06.600 --> 00:29:14.480
+people tried in the uh in the Dropout
+
+00:29:10.600 --> 00:29:17.600
+paper and this is one way to get
+
+00:29:14.480 --> 00:29:19.640
+multiple models uh and actually you can
+
+00:29:17.600 --> 00:29:21.919
+demonstrate that this helps the original
+
+00:29:19.640 --> 00:29:24.519
+motivation behind Dropout was precisely
+
+00:29:21.919 --> 00:29:26.279
+coming from this idea of
+
+00:29:24.519 --> 00:29:29.080
+ensembling
+
+00:29:26.279 --> 00:29:31.399
+another method
+
+00:29:29.080 --> 00:29:34.799
+that has been around for a very long
+
+00:29:31.399 --> 00:29:37.760
+time it's another embling method is
+
+00:29:34.799 --> 00:29:41.919
+bagging and basically the way bagging
+
+00:29:37.760 --> 00:29:41.919
+works is you have a data
+
+00:29:44.000 --> 00:29:50.159
+set like this and you just resample the
+
+00:29:47.519 --> 00:29:52.919
+data set so you sample all of the output
+
+00:29:50.159 --> 00:29:55.200
+with uh replacement and you get another
+
+00:29:52.919 --> 00:29:57.799
+data set of equal size and then you
+
+00:29:55.200 --> 00:29:58.559
+train on this but you do that like 10
+
+00:29:57.799 --> 00:30:00.120
+times
+
+00:29:58.559 --> 00:30:02.679
+and you train 10 different models and
+
+00:30:00.120 --> 00:30:04.360
+then you emble those models together and
+
+00:30:02.679 --> 00:30:06.000
+so this is another way to get multiple
+
+00:30:04.360 --> 00:30:07.519
+models and both of these still improve
+
+00:30:06.000 --> 00:30:09.640
+your robustness because they basically
+
+00:30:07.519 --> 00:30:11.440
+get a different view on the data so they
+
+00:30:09.640 --> 00:30:13.440
+smooth over some of the
+
+00:30:11.440 --> 00:30:15.360
+idiosyncrasies um and as I mentioned
+
+00:30:13.440 --> 00:30:17.960
+before you can also get multiple models
+
+00:30:15.360 --> 00:30:20.120
+from different checkpoints and then uh
+
+00:30:17.960 --> 00:30:22.159
+put them together and all of these
+
+00:30:20.120 --> 00:30:24.159
+methods are pretty related both of them
+
+00:30:22.159 --> 00:30:25.960
+basically what they're doing is they're
+
+00:30:24.159 --> 00:30:28.279
+taking advantage of the fact that you
+
+00:30:25.960 --> 00:30:29.919
+have particular models that saw
+
+00:30:28.279 --> 00:30:32.760
+different data or saw data in a
+
+00:30:29.919 --> 00:30:34.120
+different order or different nodes saw
+
+00:30:32.760 --> 00:30:35.679
+different parts of the data because you
+
+00:30:34.120 --> 00:30:37.799
+dropped out some of the nodes when they
+
+00:30:35.679 --> 00:30:41.840
+were back propping on particular
+
+00:30:37.799 --> 00:30:44.840
+varieties of the data so um even things
+
+00:30:41.840 --> 00:30:46.720
+like this can give you models that are
+
+00:30:44.840 --> 00:30:49.760
+different enough that to help uh when
+
+00:30:46.720 --> 00:30:49.760
+you're onbling or
+
+00:30:52.559 --> 00:30:59.360
+combining and then of course um you can
+
+00:30:56.919 --> 00:31:00.799
+also
+
+00:30:59.360 --> 00:31:02.480
+then of course you can also combine
+
+00:31:00.799 --> 00:31:06.960
+together like very different models like
+
+00:31:02.480 --> 00:31:06.960
+this and that also works in different
+
+00:31:07.240 --> 00:31:11.159
+ways
+
+00:31:09.000 --> 00:31:13.039
+cool part of the reason why I wanted to
+
+00:31:11.159 --> 00:31:15.320
+mention that Dropout though in
+
+00:31:13.039 --> 00:31:17.120
+particular is there's also other
+
+00:31:15.320 --> 00:31:19.240
+efficient methods for using multiple
+
+00:31:17.120 --> 00:31:22.000
+models so the big problem with
+
+00:31:19.240 --> 00:31:25.399
+ensembling is the cost
+
+00:31:22.000 --> 00:31:27.159
+and simple ensembling is very expensive
+
+00:31:25.399 --> 00:31:29.240
+because it requires you to run multiple
+
+00:31:27.159 --> 00:31:30.519
+models at test test time at inference
+
+00:31:29.240 --> 00:31:33.720
+time and this is something you don't
+
+00:31:30.519 --> 00:31:35.279
+want to be doing if you're you know
+
+00:31:33.720 --> 00:31:38.679
+deploying a service or something because
+
+00:31:35.279 --> 00:31:41.080
+it like linearly increases your cost by
+
+00:31:38.679 --> 00:31:45.200
+um the amount of bottles that you're
+
+00:31:41.080 --> 00:31:47.799
+running and it requires both end times
+
+00:31:45.200 --> 00:31:50.120
+of computation and end times of memory
+
+00:31:47.799 --> 00:31:51.720
+and memory is actually probably the
+
+00:31:50.120 --> 00:31:54.279
+worst thing because you need to deploy
+
+00:31:51.720 --> 00:31:58.159
+extra GPU machines and other stuff like
+
+00:31:54.279 --> 00:31:59.880
+that so um the question is is there any
+
+00:31:58.159 --> 00:32:03.279
+way we can get some of the benefits of
+
+00:31:59.880 --> 00:32:06.519
+embling without having to create
+
+00:32:03.279 --> 00:32:07.320
+multiple models and luckily the answer
+
+00:32:06.519 --> 00:32:09.240
+is
+
+00:32:07.320 --> 00:32:11.919
+yes
+
+00:32:09.240 --> 00:32:13.960
+the method the easiest method for doing
+
+00:32:11.919 --> 00:32:16.600
+so is something called parameter
+
+00:32:13.960 --> 00:32:18.399
+averaging and basically what you do is
+
+00:32:16.600 --> 00:32:21.960
+you just average the parameters of
+
+00:32:18.399 --> 00:32:26.039
+multiple models together um this only
+
+00:32:21.960 --> 00:32:29.200
+works under certain conditions so does
+
+00:32:26.039 --> 00:32:31.120
+anyone um does anyone know what these
+
+00:32:29.200 --> 00:32:33.320
+conditions might be there's a few
+
+00:32:31.120 --> 00:32:35.919
+obvious ones and maybe a few slightly
+
+00:32:33.320 --> 00:32:35.919
+less obvious
+
+00:32:36.039 --> 00:32:40.799
+ones so like first question do you think
+
+00:32:38.799 --> 00:32:41.919
+you could combine together do you think
+
+00:32:40.799 --> 00:32:45.880
+you could average together the
+
+00:32:41.919 --> 00:32:45.880
+parameters of llama 7B and Lama
+
+00:32:46.440 --> 00:32:52.639
+70b
+
+00:32:48.480 --> 00:32:52.639
+no the answer is no but why
+
+00:32:54.480 --> 00:32:58.440
+not I mean what does that even mean in
+
+00:32:56.760 --> 00:33:00.480
+the first place right like they have
+
+00:32:58.440 --> 00:33:02.799
+totally different numbers of parameters
+
+00:33:00.480 --> 00:33:05.840
+uh you wouldn't be able to find a one
+
+00:33:02.799 --> 00:33:07.840
+toone association between 7B and like 7
+
+00:33:05.840 --> 00:33:12.320
+billion parameters and 70 billion
+
+00:33:07.840 --> 00:33:16.880
+parameters um what about averaging
+
+00:33:12.320 --> 00:33:19.399
+together uh let's let's say llama 7B and
+
+00:33:16.880 --> 00:33:19.399
+mistol
+
+00:33:23.080 --> 00:33:29.760
+7bs yes no y I'm guessing that like for
+
+00:33:27.440 --> 00:33:29.760
+the
+
+00:33:33.760 --> 00:33:38.120
+yeah for different architectures the um
+
+00:33:36.760 --> 00:33:41.799
+the parameters could mean different
+
+00:33:38.120 --> 00:33:44.159
+things and even if the architecture is
+
+00:33:41.799 --> 00:33:45.880
+exactly the same even if your random
+
+00:33:44.159 --> 00:33:49.880
+initialization is different then that
+
+00:33:45.880 --> 00:33:52.360
+would be a disastrous because basically
+
+00:33:49.880 --> 00:33:54.760
+in neural networks there's no inherent
+
+00:33:52.360 --> 00:33:58.559
+meaning to like parameter number one
+
+00:33:54.760 --> 00:34:01.919
+right um and there's the idea of permut
+
+00:33:58.559 --> 00:34:06.679
+Inari which is
+
+00:34:01.919 --> 00:34:07.639
+um you could like randomly Swap all of
+
+00:34:06.679 --> 00:34:10.280
+the
+
+00:34:07.639 --> 00:34:12.079
+dimensions uh between within a neural
+
+00:34:10.280 --> 00:34:14.760
+network and get exactly the same
+
+00:34:12.079 --> 00:34:17.919
+function
+
+00:34:14.760 --> 00:34:22.560
+uh as long as kind
+
+00:34:17.919 --> 00:34:24.839
+of in layer number one you swap and then
+
+00:34:22.560 --> 00:34:30.359
+also take the inputs in the next layer
+
+00:34:24.839 --> 00:34:30.359
+also so um you know you know as long
+
+00:34:30.960 --> 00:34:36.399
+as if you have a weight Matrix that
+
+00:34:33.679 --> 00:34:40.800
+results in the um in the outputs being
+
+00:34:36.399 --> 00:34:49.639
+ordered like 1 two three four
+
+00:34:40.800 --> 00:34:54.159
+five one or 2 1 3 five four as long as
+
+00:34:49.639 --> 00:34:55.720
+you also swap the input direct input
+
+00:34:54.159 --> 00:34:58.400
+dimensions of this weight Matrix you get
+
+00:34:55.720 --> 00:35:01.520
+exactly the same because they
+
+00:34:58.400 --> 00:35:04.200
+linear combinations of the parameters
+
+00:35:01.520 --> 00:35:06.480
+together so neural networks have this
+
+00:35:04.200 --> 00:35:08.599
+feature of permutation and variance so
+
+00:35:06.480 --> 00:35:11.800
+models that were trained from like
+
+00:35:08.599 --> 00:35:13.280
+different uh different initializations
+
+00:35:11.800 --> 00:35:15.040
+won't be able to be combined together in
+
+00:35:13.280 --> 00:35:18.320
+this
+
+00:35:15.040 --> 00:35:20.079
+way um but the good luck the good thing
+
+00:35:18.320 --> 00:35:21.359
+is actually we have a whole bunch of
+
+00:35:20.079 --> 00:35:25.320
+models that come from the same
+
+00:35:21.359 --> 00:35:26.720
+pre-trained model right uh so we we have
+
+00:35:25.320 --> 00:35:28.640
+this initialization here this
+
+00:35:26.720 --> 00:35:31.280
+initialization was used to train Lama
+
+00:35:28.640 --> 00:35:32.920
+27b but now we have like hundreds
+
+00:35:31.280 --> 00:35:34.440
+hundreds of models that are DED from
+
+00:35:32.920 --> 00:35:37.400
+Lama 2 we have hundreds of models that
+
+00:35:34.440 --> 00:35:39.599
+are DED from mixol and there all of the
+
+00:35:37.400 --> 00:35:40.920
+dimensions actually mean the same thing
+
+00:35:39.599 --> 00:35:43.280
+because they're derived from the same
+
+00:35:40.920 --> 00:35:46.680
+parameters in the first place so those
+
+00:35:43.280 --> 00:35:48.119
+ones we can average together and um
+
+00:35:46.680 --> 00:35:50.359
+there's basically two ways that we can
+
+00:35:48.119 --> 00:35:53.520
+do this uh one is by averaging together
+
+00:35:50.359 --> 00:35:55.240
+multiple checkpoints during training so
+
+00:35:53.520 --> 00:35:57.960
+originally this was the big thing that
+
+00:35:55.240 --> 00:36:00.359
+people did uh like you would train model
+
+00:35:57.960 --> 00:36:02.119
+from scratch for a really long time but
+
+00:36:00.359 --> 00:36:03.920
+then you would take the final five
+
+00:36:02.119 --> 00:36:07.520
+checkpoints and you would just average
+
+00:36:03.920 --> 00:36:09.280
+them together and this helps reduce some
+
+00:36:07.520 --> 00:36:11.040
+of the noise that you get from
+
+00:36:09.280 --> 00:36:13.839
+stochastic gradient descent and can
+
+00:36:11.040 --> 00:36:15.520
+improve your overall accuracy if you're
+
+00:36:13.839 --> 00:36:17.280
+fine-tuning any models this is something
+
+00:36:15.520 --> 00:36:18.680
+you can do also uh because you're
+
+00:36:17.280 --> 00:36:19.800
+probably going to be saving checkpoints
+
+00:36:18.680 --> 00:36:21.160
+you can just take the best five
+
+00:36:19.800 --> 00:36:23.079
+checkpoints and average them together
+
+00:36:21.160 --> 00:36:27.280
+and that actually can improve your
+
+00:36:23.079 --> 00:36:28.160
+accuracy quite a bit um another thing is
+
+00:36:27.280 --> 00:36:31.520
+find
+
+00:36:28.160 --> 00:36:32.880
+uh tuned model merging soine tune um in
+
+00:36:31.520 --> 00:36:35.000
+several ways and then merge them
+
+00:36:32.880 --> 00:36:39.079
+together and so for example we might
+
+00:36:35.000 --> 00:36:41.240
+take Lama 27b instruct and um vuna 7B
+
+00:36:39.079 --> 00:36:44.760
+1.5 and merg them together with some
+
+00:36:41.240 --> 00:36:47.599
+weights and uh we could you
+
+00:36:44.760 --> 00:36:50.319
+know smooth over their idos synchr dises
+
+00:36:47.599 --> 00:36:52.520
+and get better results
+
+00:36:50.319 --> 00:36:56.280
+too
+
+00:36:52.520 --> 00:36:56.280
+cool uh any questions
+
+00:36:56.520 --> 00:36:59.520
+here
+
+00:37:00.920 --> 00:37:03.119
+oh
+
+00:37:04.680 --> 00:37:11.920
+yeah want to so I just
+
+00:37:09.680 --> 00:37:14.079
+came
+
+00:37:11.920 --> 00:37:19.040
+non I
+
+00:37:14.079 --> 00:37:19.040
+use like those different chain and
+
+00:37:19.640 --> 00:37:23.319
+just
+
+00:37:21.160 --> 00:37:26.640
+I pretty
+
+00:37:23.319 --> 00:37:29.520
+efficient because on the same model you
+
+00:37:26.640 --> 00:37:29.520
+get
+
+00:37:35.640 --> 00:37:40.839
+yeah so would this would this parameter
+
+00:37:38.000 --> 00:37:46.119
+averaging be a good method for U making
+
+00:37:40.839 --> 00:37:49.839
+a model less toxic for example the
+
+00:37:46.119 --> 00:37:53.200
+answer is a little bit trickier there I
+
+00:37:49.839 --> 00:37:56.119
+guess because um I I feel like this is
+
+00:37:53.200 --> 00:37:58.160
+good for mixing two models together so
+
+00:37:56.119 --> 00:38:01.400
+if you're mixing your
+
+00:37:58.160 --> 00:38:03.359
+like non-toxicity tuned model or your
+
+00:38:01.400 --> 00:38:06.079
+safety tuned model with the original
+
+00:38:03.359 --> 00:38:07.520
+base model that was not uh safety tuned
+
+00:38:06.079 --> 00:38:08.800
+or something like that then you might
+
+00:38:07.520 --> 00:38:11.240
+get something in the middle so you might
+
+00:38:08.800 --> 00:38:13.319
+get something that's less safe than the
+
+00:38:11.240 --> 00:38:18.720
+uh like the model that was tuned to not
+
+00:38:13.319 --> 00:38:21.400
+be toxic so it might be uh yeah I'm not
+
+00:38:18.720 --> 00:38:23.920
+sure but like let's say you let's say
+
+00:38:21.400 --> 00:38:26.240
+you have a model that somebody
+
+00:38:23.920 --> 00:38:28.640
+else did like a really good job
+
+00:38:26.240 --> 00:38:31.359
+instruction tuning for you
+
+00:38:28.640 --> 00:38:33.640
+um and anytime you start using safety
+
+00:38:31.359 --> 00:38:35.560
+tuning on it you like hurt the
+
+00:38:33.640 --> 00:38:38.680
+instruction tuning like the model gets
+
+00:38:35.560 --> 00:38:40.560
+worse I could see a world where you take
+
+00:38:38.680 --> 00:38:43.000
+the base model the same base model you
+
+00:38:40.560 --> 00:38:45.280
+take llama 27b you train like a less
+
+00:38:43.000 --> 00:38:47.480
+toxic version of llama 27d and then do
+
+00:38:45.280 --> 00:38:51.319
+parameter averaging with the like well
+
+00:38:47.480 --> 00:38:53.160
+instruction tuned model um that might
+
+00:38:51.319 --> 00:38:55.359
+work that might make something that's
+
+00:38:53.160 --> 00:38:57.560
+more safe and like not much worse
+
+00:38:55.359 --> 00:39:01.440
+instruction to so there's definitely I
+
+00:38:57.560 --> 00:39:01.440
+think creative things that you can do
+
+00:39:01.520 --> 00:39:08.400
+that um maybe I'll go directly into the
+
+00:39:04.960 --> 00:39:11.480
+methods um
+
+00:39:08.400 --> 00:39:13.240
+so uh there's a few uh recent papers on
+
+00:39:11.480 --> 00:39:16.000
+this like this method has been around
+
+00:39:13.240 --> 00:39:17.880
+for a long time since at least 1996 but
+
+00:39:16.000 --> 00:39:20.880
+uh recently people have examined it a
+
+00:39:17.880 --> 00:39:24.800
+lot in the context of uh kind of modern
+
+00:39:20.880 --> 00:39:27.400
+networks and uh this paper model soup uh
+
+00:39:24.800 --> 00:39:29.000
+examines two strategies the first one is
+
+00:39:27.400 --> 00:39:31.400
+uniform averaging where you just average
+
+00:39:29.000 --> 00:39:33.560
+all the parameters together uh like as
+
+00:39:31.400 --> 00:39:35.480
+you would expect but they also have a
+
+00:39:33.560 --> 00:39:38.319
+greedy averaging method and basically
+
+00:39:35.480 --> 00:39:40.240
+what they do here is they add one model
+
+00:39:38.319 --> 00:39:42.119
+and check if the whole like averaged
+
+00:39:40.240 --> 00:39:43.680
+model improves and then only if the
+
+00:39:42.119 --> 00:39:45.760
+whole averaged model improves do they
+
+00:39:43.680 --> 00:39:49.040
+keep that model otherwise they throw it
+
+00:39:45.760 --> 00:39:52.960
+out and then they um they don't uh use
+
+00:39:49.040 --> 00:39:54.520
+it so what they demonstrate uh this is a
+
+00:39:52.960 --> 00:39:57.560
+little bit small but basically the
+
+00:39:54.520 --> 00:40:00.520
+purple star here is uh when the use
+
+00:39:57.560 --> 00:40:02.480
+greedy averaging and then the blue
+
+00:40:00.520 --> 00:40:05.119
+circle here is when they use the uniform
+
+00:40:02.480 --> 00:40:08.280
+averaging and then green is all of the
+
+00:40:05.119 --> 00:40:09.960
+models that they they put into this
+
+00:40:08.280 --> 00:40:12.560
+average
+
+00:40:09.960 --> 00:40:16.680
+and what they found
+
+00:40:12.560 --> 00:40:18.480
+is this is average uh accuracy on image
+
+00:40:16.680 --> 00:40:22.400
+net which is the thing that they they
+
+00:40:18.480 --> 00:40:25.160
+used in deciding which models to merge
+
+00:40:22.400 --> 00:40:26.920
+in greedily and then this is on
+
+00:40:25.160 --> 00:40:28.640
+distribution shifts so this is on other
+
+00:40:26.920 --> 00:40:31.119
+data sets other than the ones they use
+
+00:40:28.640 --> 00:40:33.040
+specifically for training and what you
+
+00:40:31.119 --> 00:40:34.720
+can see is the greedy averaging method
+
+00:40:33.040 --> 00:40:38.720
+does
+
+00:40:34.720 --> 00:40:40.839
+better um than the best single model on
+
+00:40:38.720 --> 00:40:42.319
+the data set that they used to decide
+
+00:40:40.839 --> 00:40:44.800
+that greedy
+
+00:40:42.319 --> 00:40:46.560
+average the uniform average actually
+
+00:40:44.800 --> 00:40:48.359
+does worse than the best model so you
+
+00:40:46.560 --> 00:40:50.960
+would actually be better off for image
+
+00:40:48.359 --> 00:40:52.960
+net accuracy to just use the best model
+
+00:40:50.960 --> 00:40:56.000
+but it's more robust so on the
+
+00:40:52.960 --> 00:40:57.319
+distribution shift like data set it
+
+00:40:56.000 --> 00:41:00.000
+actually does better than any of them
+
+00:40:57.319 --> 00:41:02.280
+models so um you can see that there's
+
+00:41:00.000 --> 00:41:04.720
+kind of trade-offs between choosing
+
+00:41:02.280 --> 00:41:06.480
+those
+
+00:41:04.720 --> 00:41:09.319
+essentially
+
+00:41:06.480 --> 00:41:12.040
+um whoops that's a that's a typo that
+
+00:41:09.319 --> 00:41:15.760
+should be ensembling but um they also
+
+00:41:12.040 --> 00:41:18.440
+demonstrate that um averaging is
+
+00:41:15.760 --> 00:41:22.720
+correlated with ensembling so this is
+
+00:41:18.440 --> 00:41:25.200
+the um image accuracy of the parameter
+
+00:41:22.720 --> 00:41:27.000
+average model this is image not accuracy
+
+00:41:25.200 --> 00:41:30.200
+of the Ensemble so this is actually I
+
+00:41:27.000 --> 00:41:33.720
+think really interesting figure um what
+
+00:41:30.200 --> 00:41:36.440
+it shows is that there's a pretty strong
+
+00:41:33.720 --> 00:41:38.760
+correlation between the two averaging is
+
+00:41:36.440 --> 00:41:41.400
+almost never better than ensembling the
+
+00:41:38.760 --> 00:41:44.800
+two together but it's faster of course
+
+00:41:41.400 --> 00:41:48.119
+so it's better because it's faster and
+
+00:41:44.800 --> 00:41:50.000
+there are situations where the Ensemble
+
+00:41:48.119 --> 00:41:51.680
+is much better than the average model so
+
+00:41:50.000 --> 00:41:55.720
+like the average model hurts the
+
+00:41:51.680 --> 00:41:58.560
+averaging hurts um onbling does not hurt
+
+00:41:55.720 --> 00:42:01.319
+so what this shows you is parameter
+
+00:41:58.560 --> 00:42:03.119
+averaging is is safe and it nearly
+
+00:42:01.319 --> 00:42:04.359
+approximates model on samping most of
+
+00:42:03.119 --> 00:42:06.720
+the time but there are cases where it
+
+00:42:04.359 --> 00:42:08.119
+doesn't so you do need to be a little
+
+00:42:06.720 --> 00:42:11.720
+bit careful and it might hurt your
+
+00:42:08.119 --> 00:42:11.720
+accuracy in some cases
+
+00:42:16.680 --> 00:42:21.520
+yeah oh yeah sorry very good point yes
+
+00:42:19.280 --> 00:42:21.520
+it's
+
+00:42:22.319 --> 00:42:29.119
+paralel yeah
+
+00:42:26.119 --> 00:42:29.119
+this
+
+00:42:36.480 --> 00:42:41.520
+um how do you know
+
+00:42:39.400 --> 00:42:45.720
+it's
+
+00:42:41.520 --> 00:42:48.280
+particular yeah so notably all of these
+
+00:42:45.720 --> 00:42:48.280
+are
+
+00:42:48.800 --> 00:42:52.240
+initialized it's been a little while
+
+00:42:50.800 --> 00:42:54.079
+since I read this but I know all of
+
+00:42:52.240 --> 00:42:56.520
+these were initialized from a model that
+
+00:42:54.079 --> 00:42:58.160
+was already pretty good on image that
+
+00:42:56.520 --> 00:43:01.760
+and then they were tuned in different
+
+00:42:58.160 --> 00:43:03.800
+ways I guess and so this I think this
+
+00:43:01.760 --> 00:43:05.319
+might be initialized with a model that
+
+00:43:03.800 --> 00:43:09.160
+was trained on a different data set or
+
+00:43:05.319 --> 00:43:10.160
+something like that um and so they are
+
+00:43:09.160 --> 00:43:12.480
+all starting from the same
+
+00:43:10.160 --> 00:43:14.480
+initialization so parameter U
+
+00:43:12.480 --> 00:43:16.599
+permutation inv variance is not an issue
+
+00:43:14.480 --> 00:43:19.200
+there because they're starting from the
+
+00:43:16.599 --> 00:43:23.480
+pre um but despite the fact that it's
+
+00:43:19.200 --> 00:43:26.520
+not a problem there are there are cases
+
+00:43:23.480 --> 00:43:29.119
+where like averaging is detrimental
+
+00:43:26.520 --> 00:43:29.119
+compared to
+
+00:43:32.839 --> 00:43:37.559
+um okay so
+
+00:43:42.800 --> 00:43:45.800
+yeah
+
+00:43:51.720 --> 00:43:54.720
+yep
+
+00:43:56.040 --> 00:43:59.040
+y
+
+00:44:07.079 --> 00:44:10.079
+okay
+
+00:44:26.040 --> 00:44:29.040
+y
+
+00:44:46.319 --> 00:44:52.520
+yeah so that's a great question um I'll
+
+00:44:48.240 --> 00:44:54.920
+just repeat it which is um the these
+
+00:44:52.520 --> 00:44:57.520
+experiments were done on CNN's or image
+
+00:44:54.920 --> 00:44:59.280
+net like uh CNN based image that
+
+00:44:57.520 --> 00:45:01.119
+classifiers is there something different
+
+00:44:59.280 --> 00:45:04.040
+than Transformers particularly because
+
+00:45:01.119 --> 00:45:06.240
+Transformer representations tend to be
+
+00:45:04.040 --> 00:45:09.000
+uh like very concentrated in particular
+
+00:45:06.240 --> 00:45:11.359
+parts of the space that's an excellent
+
+00:45:09.000 --> 00:45:14.040
+question um what I do know is a lot of
+
+00:45:11.359 --> 00:45:15.319
+people do merge together Transformer
+
+00:45:14.040 --> 00:45:18.319
+models in fact if you look at the
+
+00:45:15.319 --> 00:45:20.079
+hugging face leaderboard there's like
+
+00:45:18.319 --> 00:45:22.240
+something and something merg together
+
+00:45:20.079 --> 00:45:24.200
+like all over the leader board and it
+
+00:45:22.240 --> 00:45:25.960
+does tend to improve accuracy so I I
+
+00:45:24.200 --> 00:45:27.480
+know it is definitely effective for
+
+00:45:25.960 --> 00:45:28.559
+Transformers
+
+00:45:27.480 --> 00:45:32.040
+however Are
+
+00:45:28.559 --> 00:45:34.640
+there specific model like parameter
+
+00:45:32.040 --> 00:45:37.040
+averaging or model merging methods that
+
+00:45:34.640 --> 00:45:38.599
+could improve accuracy by taking
+
+00:45:37.040 --> 00:45:40.680
+advantage of the fact that Transformers
+
+00:45:38.599 --> 00:45:42.480
+behaving a c certain way I think that's
+
+00:45:40.680 --> 00:45:44.920
+totally possible and you know it would
+
+00:45:42.480 --> 00:45:48.800
+be an interesting research Direction um
+
+00:45:44.920 --> 00:45:51.680
+I'm not familiar enough with that
+
+00:45:48.800 --> 00:45:53.359
+particular part myself to say oh I have
+
+00:45:51.680 --> 00:45:55.160
+this great idea that you should work on
+
+00:45:53.359 --> 00:45:55.920
+but I think if you're interested in it
+
+00:45:55.160 --> 00:45:58.160
+you
+
+00:45:55.920 --> 00:46:00.280
+definitely
+
+00:45:58.160 --> 00:46:05.240
+cool anything
+
+00:46:00.280 --> 00:46:08.920
+El okay so there's also the idea of uh
+
+00:46:05.240 --> 00:46:12.440
+task vectors and um basically task
+
+00:46:08.920 --> 00:46:15.280
+vectors here we are just merging
+
+00:46:12.440 --> 00:46:17.280
+together two models by taking the
+
+00:46:15.280 --> 00:46:18.280
+parameters of the models and averaging
+
+00:46:17.280 --> 00:46:22.079
+them
+
+00:46:18.280 --> 00:46:24.480
+together task vectors and other related
+
+00:46:22.079 --> 00:46:26.040
+works specifically take advantage of the
+
+00:46:24.480 --> 00:46:27.640
+fact that we're looking at different
+
+00:46:26.040 --> 00:46:29.160
+fine-tuned models
+
+00:46:27.640 --> 00:46:31.480
+and so these are models where we have a
+
+00:46:29.160 --> 00:46:33.920
+base model and we know that uh that we
+
+00:46:31.480 --> 00:46:35.760
+fine-tuned from this base model and the
+
+00:46:33.920 --> 00:46:38.480
+basic idea is that we have our base
+
+00:46:35.760 --> 00:46:40.319
+model here and the task Vector is the
+
+00:46:38.480 --> 00:46:43.280
+difference between the base models
+
+00:46:40.319 --> 00:46:45.559
+Vector uh parameters and the uh fine
+
+00:46:43.280 --> 00:46:49.480
+tune models parameters so that's what
+
+00:46:45.559 --> 00:46:52.720
+they Define as a task Vector um what
+
+00:46:49.480 --> 00:46:56.000
+does this allow us to do this allows us
+
+00:46:52.720 --> 00:46:58.040
+to do a number of interesting things um
+
+00:46:56.000 --> 00:47:02.359
+the first one
+
+00:46:58.040 --> 00:47:05.119
+is that we can actually subtract out uh
+
+00:47:02.359 --> 00:47:08.960
+quote unquote tasks that we don't want
+
+00:47:05.119 --> 00:47:11.559
+so like let's say we had a model that
+
+00:47:08.960 --> 00:47:13.440
+was trained on lots of toxic text or we
+
+00:47:11.559 --> 00:47:15.760
+had a model that was trained on lots of
+
+00:47:13.440 --> 00:47:18.760
+private text or something like that we
+
+00:47:15.760 --> 00:47:22.040
+could actually subtract out the task
+
+00:47:18.760 --> 00:47:24.240
+Vector from this and basically attempt
+
+00:47:22.040 --> 00:47:27.480
+to remove the model's ability to uh do
+
+00:47:24.240 --> 00:47:31.240
+that sort of things um you can also
+
+00:47:27.480 --> 00:47:36.040
+take two task vectors and combine them
+
+00:47:31.240 --> 00:47:39.280
+together and uh like get the model uh
+
+00:47:36.040 --> 00:47:42.200
+from the combination of the two um this
+
+00:47:39.280 --> 00:47:44.280
+isn't exactly the same as averaging the
+
+00:47:42.200 --> 00:47:45.440
+parameters because if you average the
+
+00:47:44.280 --> 00:47:47.400
+parameters you would probably get
+
+00:47:45.440 --> 00:47:49.160
+something in the middle right here but
+
+00:47:47.400 --> 00:47:50.440
+if you average the two vectors or add
+
+00:47:49.160 --> 00:47:52.040
+the two vectors together you would get
+
+00:47:50.440 --> 00:47:53.760
+something over here actually sorry if
+
+00:47:52.040 --> 00:47:56.520
+you average the vectors maybe it's the
+
+00:47:53.760 --> 00:47:58.119
+same so you could like add together the
+
+00:47:56.520 --> 00:47:59.480
+two vectors and and that would be
+
+00:47:58.119 --> 00:48:01.640
+something different than taking the
+
+00:47:59.480 --> 00:48:05.280
+average so it gives you a little bit
+
+00:48:01.640 --> 00:48:07.720
+more flexibility about things to do
+
+00:48:05.280 --> 00:48:09.599
+um and another thing this allows you to
+
+00:48:07.720 --> 00:48:12.920
+do is this allows you to try to resolve
+
+00:48:09.599 --> 00:48:15.400
+conflicts between um vectors of
+
+00:48:12.920 --> 00:48:19.720
+different tasks and so this is an
+
+00:48:15.400 --> 00:48:22.480
+illustration of of this method here
+
+00:48:19.720 --> 00:48:25.680
+and this has three tasks basically it
+
+00:48:22.480 --> 00:48:27.720
+has model one model two model three and
+
+00:48:25.680 --> 00:48:29.920
+each of them has vectors and you'll see
+
+00:48:27.720 --> 00:48:32.880
+that in some cases these vectors
+
+00:48:29.920 --> 00:48:34.599
+conflict so we have like pink going up
+
+00:48:32.880 --> 00:48:36.079
+we have yellow and purple going down we
+
+00:48:34.599 --> 00:48:37.800
+have yellow going up we have pink and
+
+00:48:36.079 --> 00:48:40.720
+purple going down etc
+
+00:48:37.800 --> 00:48:43.040
+etc and what this does is this
+
+00:48:40.720 --> 00:48:45.960
+identifies the vectors that are uh
+
+00:48:43.040 --> 00:48:48.040
+pointing the most strongly in particular
+
+00:48:45.960 --> 00:48:50.440
+directions and then it resolves
+
+00:48:48.040 --> 00:48:52.240
+conflicts between them and comes up with
+
+00:48:50.440 --> 00:48:54.559
+a vector that tries to move in a
+
+00:48:52.240 --> 00:48:55.920
+direction that improves all of the tasks
+
+00:48:54.559 --> 00:48:59.319
+at the same time and they demonstrate
+
+00:48:55.920 --> 00:49:01.480
+that this is better method for um kind
+
+00:48:59.319 --> 00:49:04.599
+of improving the ability to do all of
+
+00:49:01.480 --> 00:49:09.599
+the tasks compared to just averaging
+
+00:49:04.599 --> 00:49:09.599
+things together so yeah first
+
+00:49:11.920 --> 00:49:15.559
+exle like it just
+
+00:49:16.880 --> 00:49:23.640
+add yeah so this is
+
+00:49:20.680 --> 00:49:25.760
+um yeah you could move it more in that
+
+00:49:23.640 --> 00:49:27.319
+direction it there's obviously no
+
+00:49:25.760 --> 00:49:29.720
+guarantee that it would make it better
+
+00:49:27.319 --> 00:49:32.319
+but it might make it more extreme at
+
+00:49:29.720 --> 00:49:35.760
+least so uh
+
+00:49:32.319 --> 00:49:35.760
+yeah any other
+
+00:49:36.680 --> 00:49:39.960
+questions all
+
+00:49:55.640 --> 00:49:58.640
+yes
+
+00:50:25.640 --> 00:50:28.640
+one
+
+00:50:32.319 --> 00:50:37.240
+yeah yeah so this is a a great question
+
+00:50:35.599 --> 00:50:38.760
+um I can explain a little bit I'm not
+
+00:50:37.240 --> 00:50:40.760
+going to talk about Metal learning
+
+00:50:38.760 --> 00:50:42.680
+extensively in this class but just to
+
+00:50:40.760 --> 00:50:46.040
+give a very quick primer for people who
+
+00:50:42.680 --> 00:50:46.040
+don't know about it
+
+00:50:55.640 --> 00:50:58.640
+um
+
+00:51:00.359 --> 00:51:06.040
+this is an example of a paper on metal
+
+00:51:03.319 --> 00:51:09.559
+learning for low resource machine
+
+00:51:06.040 --> 00:51:12.680
+translation um I you can take a look at
+
+00:51:09.559 --> 00:51:16.200
+this paper um or not take a look at this
+
+00:51:12.680 --> 00:51:17.760
+paper um uh but the reason why I wanted
+
+00:51:16.200 --> 00:51:20.799
+to look at this paper is because it has
+
+00:51:17.760 --> 00:51:25.160
+a good um uh it has a good illustration
+
+00:51:20.799 --> 00:51:27.200
+of what metal learning is and basically
+
+00:51:25.160 --> 00:51:29.160
+um if we
+
+00:51:27.200 --> 00:51:33.839
+are doing transfer learning from a
+
+00:51:29.160 --> 00:51:35.880
+single task what we do is we have like a
+
+00:51:33.839 --> 00:51:37.960
+Spanish English machine translation
+
+00:51:35.880 --> 00:51:41.839
+system and then we fine-tune it to try
+
+00:51:37.960 --> 00:51:45.280
+to hit like to try to be a good Romanian
+
+00:51:41.839 --> 00:51:48.680
+uh English or latan English system if
+
+00:51:45.280 --> 00:51:50.400
+we're doing multitask learning um or
+
+00:51:48.680 --> 00:51:53.079
+which also could be equivalent to like
+
+00:51:50.400 --> 00:51:55.680
+instruction tuning for example we have
+
+00:51:53.079 --> 00:51:57.680
+uh French uh Spanish and Portuguese we
+
+00:51:55.680 --> 00:52:03.319
+train on all the then we
+
+00:51:57.680 --> 00:52:06.520
+fine-tune to uh to be a good Romanian uh
+
+00:52:03.319 --> 00:52:09.240
+translator latan trans uh
+
+00:52:06.520 --> 00:52:10.760
+translator whereas metal learning what
+
+00:52:09.240 --> 00:52:12.119
+it's trying to do is it's trying to
+
+00:52:10.760 --> 00:52:14.680
+learn a good
+
+00:52:12.119 --> 00:52:17.480
+initialization that makes it easy to
+
+00:52:14.680 --> 00:52:21.280
+fine-tune to try to come up with a model
+
+00:52:17.480 --> 00:52:23.839
+that is good uh for fine-tuning into new
+
+00:52:21.280 --> 00:52:29.040
+tasks
+
+00:52:23.839 --> 00:52:32.200
+um the way you do this is basically um
+
+00:52:29.040 --> 00:52:36.599
+you have two
+
+00:52:32.200 --> 00:52:39.400
+steps um of gradient descent and so you
+
+00:52:36.599 --> 00:52:42.400
+have a first step where you uh train the
+
+00:52:39.400 --> 00:52:42.400
+model
+
+00:52:42.599 --> 00:52:50.160
+um where you have an update on like data
+
+00:52:47.119 --> 00:52:50.160
+from French for
+
+00:52:55.440 --> 00:53:02.400
+example
+
+00:52:57.920 --> 00:53:02.400
+and then you have another
+
+00:53:04.640 --> 00:53:10.599
+update um where you train on like black
+
+00:53:07.880 --> 00:53:10.599
+or something like
+
+00:53:12.559 --> 00:53:17.040
+this and this is a very informal very
+
+00:53:15.599 --> 00:53:18.200
+informal description there's a lot of
+
+00:53:17.040 --> 00:53:19.599
+stuff we could talk about here I could
+
+00:53:18.200 --> 00:53:22.119
+have a whole class on this but we're not
+
+00:53:19.599 --> 00:53:27.200
+going to um I don't have one planned at
+
+00:53:22.119 --> 00:53:28.559
+the moment um and so you uh you up once
+
+00:53:27.200 --> 00:53:30.319
+and then you update again and you
+
+00:53:28.559 --> 00:53:33.400
+differentiate through this update
+
+00:53:30.319 --> 00:53:35.160
+process uh so that this becomes like
+
+00:53:33.400 --> 00:53:37.440
+essentially a good initialization for
+
+00:53:35.160 --> 00:53:40.640
+training on other languages or for other
+
+00:53:37.440 --> 00:53:43.000
+tasks or things like that
+
+00:53:40.640 --> 00:53:44.920
+um now going back to the original
+
+00:53:43.000 --> 00:53:46.240
+question the original question is is
+
+00:53:44.920 --> 00:53:50.000
+there a connection between metal
+
+00:53:46.240 --> 00:53:50.000
+learning in these uh task
+
+00:53:54.720 --> 00:53:58.440
+vectors I'm not
+
+00:53:59.079 --> 00:54:03.720
+100% sure about that because I think
+
+00:54:01.760 --> 00:54:06.599
+these test backs are generally created
+
+00:54:03.720 --> 00:54:08.480
+post Haw and so they're not like there's
+
+00:54:06.599 --> 00:54:12.680
+no explicit learning step to try to make
+
+00:54:08.480 --> 00:54:14.440
+them uh you know generalize well um one
+
+00:54:12.680 --> 00:54:15.960
+one thing that maybe might be
+
+00:54:14.440 --> 00:54:18.559
+interesting to people this is a paper
+
+00:54:15.960 --> 00:54:23.040
+that we like literally just put on
+
+00:54:18.559 --> 00:54:23.040
+archive about last week
+
+00:54:25.359 --> 00:54:28.359
+um
+
+00:54:34.520 --> 00:54:39.880
+and we didn't actually use metal
+
+00:54:36.400 --> 00:54:41.960
+learning in this uh in this paper um
+
+00:54:39.880 --> 00:54:44.520
+just because metal learning actually is
+
+00:54:41.960 --> 00:54:46.160
+hard to implement uh because you need to
+
+00:54:44.520 --> 00:54:48.680
+do this kind of double differentiation
+
+00:54:46.160 --> 00:54:50.720
+and can become very very expensive for
+
+00:54:48.680 --> 00:54:52.839
+large models but we did something a
+
+00:54:50.720 --> 00:54:55.920
+little bit motivated by
+
+00:54:52.839 --> 00:54:59.680
+um uh by metal learning and what we did
+
+00:54:55.920 --> 00:55:01.280
+is we took a pre-trained LM and normally
+
+00:54:59.680 --> 00:55:04.359
+what you do is something like continued
+
+00:55:01.280 --> 00:55:06.799
+pre-training on new documents to learn
+
+00:55:04.359 --> 00:55:10.160
+knowledge from the new documents or
+
+00:55:06.799 --> 00:55:12.200
+maybe um instruction tuning including
+
+00:55:10.160 --> 00:55:15.960
+instruction tuning on data on documents
+
+00:55:12.200 --> 00:55:17.520
+about the kind of uh data that you would
+
+00:55:15.960 --> 00:55:18.880
+want to be answering questions about so
+
+00:55:17.520 --> 00:55:20.640
+like let's say you're trying to train a
+
+00:55:18.880 --> 00:55:23.000
+medical language model you might train
+
+00:55:20.640 --> 00:55:26.680
+on lots of medical documents but what we
+
+00:55:23.000 --> 00:55:29.839
+did here is we had a step where we train
+
+00:55:26.680 --> 00:55:33.720
+in advance to
+
+00:55:29.839 --> 00:55:38.079
+get on question answer Pairs and
+
+00:55:33.720 --> 00:55:40.400
+documents from another domain and then
+
+00:55:38.079 --> 00:55:43.359
+we have a step after that where we train
+
+00:55:40.400 --> 00:55:46.400
+on documents from the domain we want to
+
+00:55:43.359 --> 00:55:48.400
+answer on so like we might train on
+
+00:55:46.400 --> 00:55:51.079
+Wikipedia question answer Pairs and
+
+00:55:48.400 --> 00:55:52.559
+Wikipedia documents and then in the
+
+00:55:51.079 --> 00:55:54.079
+second step we would train on medical
+
+00:55:52.559 --> 00:55:56.680
+documents and we demonstrate that
+
+00:55:54.079 --> 00:55:58.880
+basically this allows the model to do a
+
+00:55:56.680 --> 00:56:00.880
+better job of question answering over
+
+00:55:58.880 --> 00:56:03.640
+these uh documents that we find tune on
+
+00:56:00.880 --> 00:56:05.000
+over here and so kind of going back to
+
+00:56:03.640 --> 00:56:06.760
+the metal learning paper that I talked
+
+00:56:05.000 --> 00:56:08.359
+about before the metal learning paper
+
+00:56:06.760 --> 00:56:10.640
+tries to get the parameters in a good
+
+00:56:08.359 --> 00:56:12.559
+space so that after you find ton on
+
+00:56:10.640 --> 00:56:15.520
+another data set you do a good job of
+
+00:56:12.559 --> 00:56:17.799
+that in this paper our motivation is
+
+00:56:15.520 --> 00:56:20.359
+that the model kind of learns that when
+
+00:56:17.799 --> 00:56:22.039
+you train on documents you should be
+
+00:56:20.359 --> 00:56:24.079
+able to answer questions about those
+
+00:56:22.039 --> 00:56:25.480
+documents and so when you get a new set
+
+00:56:24.079 --> 00:56:27.200
+of documents it's kind of in a good part
+
+00:56:25.480 --> 00:56:31.079
+of the parameter space to make that easy
+
+00:56:27.200 --> 00:56:33.520
+to do so um if that if metal learning is
+
+00:56:31.079 --> 00:56:34.640
+interesting um there are tutorials on
+
+00:56:33.520 --> 00:56:37.119
+metal learning that I could probably
+
+00:56:34.640 --> 00:56:39.599
+share and then um if you're interested
+
+00:56:37.119 --> 00:56:42.599
+in kind of like learning Knowledge from
+
+00:56:39.599 --> 00:56:45.039
+uh learning Knowledge
+
+00:56:42.599 --> 00:56:46.079
+from continued pre-training or something
+
+00:56:45.039 --> 00:56:47.400
+like that you could take a look at this
+
+00:56:46.079 --> 00:56:49.920
+right there as
+
+00:56:47.400 --> 00:56:54.480
+well uh
+
+00:56:49.920 --> 00:56:54.480
+cool any questions about that
+
+00:56:55.240 --> 00:57:00.880
+or
+
+00:56:57.599 --> 00:57:02.480
+okay cool I I'll jump on this so anyway
+
+00:57:00.880 --> 00:57:05.520
+um I talked about several methods for
+
+00:57:02.480 --> 00:57:07.520
+merging models together um there's a
+
+00:57:05.520 --> 00:57:09.440
+popular toolkit called merge kit that
+
+00:57:07.520 --> 00:57:10.960
+makes it relatively easy to do this it
+
+00:57:09.440 --> 00:57:13.280
+implements a lot of the models that I
+
+00:57:10.960 --> 00:57:17.160
+talked about here including uh the
+
+00:57:13.280 --> 00:57:19.880
+linear methods um uh the task arithmetic
+
+00:57:17.160 --> 00:57:23.079
+method and ties uh so I talked about
+
+00:57:19.880 --> 00:57:25.480
+these there is kind of like a expansion
+
+00:57:23.079 --> 00:57:27.240
+on this so if you want to merge together
+
+00:57:25.480 --> 00:57:28.760
+models it's Rel easy to do from a
+
+00:57:27.240 --> 00:57:30.760
+software standpoint as so so you can
+
+00:57:28.760 --> 00:57:35.119
+take a look at
+
+00:57:30.760 --> 00:57:38.000
+that um another really simple thing uh
+
+00:57:35.119 --> 00:57:39.880
+is uh distilling ensembles and so we
+
+00:57:38.000 --> 00:57:43.039
+already talked about distillation the
+
+00:57:39.880 --> 00:57:45.599
+idea is simple um
+
+00:57:43.039 --> 00:57:47.680
+you so parameter averaging only really
+
+00:57:45.599 --> 00:57:49.200
+works for models within the same run uh
+
+00:57:47.680 --> 00:57:51.760
+same model architecture same
+
+00:57:49.200 --> 00:57:54.280
+initialization so knowledge distillation
+
+00:57:51.760 --> 00:57:55.559
+uh trains a model to copy The Ensemble
+
+00:57:54.280 --> 00:57:57.359
+and so it tries to match the
+
+00:57:55.559 --> 00:57:59.119
+distribution over the predicted words
+
+00:57:57.359 --> 00:58:00.760
+for an
+
+00:57:59.119 --> 00:58:05.319
+on
+
+00:58:00.760 --> 00:58:07.799
+um and so this allows the model to make
+
+00:58:05.319 --> 00:58:09.079
+the same you know good predictions as
+
+00:58:07.799 --> 00:58:11.079
+The Ensemble make the same bad
+
+00:58:09.079 --> 00:58:12.799
+predictions as Ensemble it just allows
+
+00:58:11.079 --> 00:58:14.799
+you to learn more efficiently just like
+
+00:58:12.799 --> 00:58:16.680
+distillation does in general and they
+
+00:58:14.799 --> 00:58:18.960
+actually model distillation the original
+
+00:58:16.680 --> 00:58:22.240
+motivation for it when Jeff Hinton
+
+00:58:18.960 --> 00:58:24.599
+proposed it in 2015 in in this paper was
+
+00:58:22.240 --> 00:58:25.680
+to copy an ensemble now we use it for a
+
+00:58:24.599 --> 00:58:27.039
+lot of other things like in the
+
+00:58:25.680 --> 00:58:31.160
+distillation
+
+00:58:27.039 --> 00:58:31.160
+like weed the class but was the
+
+00:58:34.119 --> 00:58:39.599
+original
+
+00:58:35.760 --> 00:58:42.640
+um next I'll move on to sparse mixture
+
+00:58:39.599 --> 00:58:44.960
+of experts models and this is really
+
+00:58:42.640 --> 00:58:47.599
+important uh this is used in a lot of
+
+00:58:44.960 --> 00:58:51.319
+modern models it's allegedly used in GPD
+
+00:58:47.599 --> 00:58:53.160
+4 um and it is uh definitely used in
+
+00:58:51.319 --> 00:58:55.280
+mixl uh which is kind of one of the
+
+00:58:53.160 --> 00:58:58.039
+state-ofthe-art open models so I think
+
+00:58:55.280 --> 00:58:58.039
+it's a good thing to know
+
+00:58:59.880 --> 00:59:05.720
+um what these do is they take advantage
+
+00:59:02.680 --> 00:59:08.160
+of sparse computation so if you think
+
+00:59:05.720 --> 00:59:09.359
+about what happens when you do a scalar
+
+00:59:08.160 --> 00:59:12.760
+tensor
+
+00:59:09.359 --> 00:59:14.720
+multiply where the scaler is zero and
+
+00:59:12.760 --> 00:59:17.160
+basically the result of the entire
+
+00:59:14.720 --> 00:59:19.680
+resulting tensor is guaranteed to be
+
+00:59:17.160 --> 00:59:21.440
+zero and so you don't even need to do
+
+00:59:19.680 --> 00:59:25.440
+the computation you don't need to even
+
+00:59:21.440 --> 00:59:27.520
+bother um and so this manifests itself
+
+00:59:25.440 --> 00:59:30.240
+in a bunch of different places in modern
+
+00:59:27.520 --> 00:59:35.000
+models um the first one could be single
+
+00:59:30.240 --> 00:59:38.400
+rows in a matrix multiply so um if you
+
+00:59:35.000 --> 00:59:40.480
+have a big Matrix multiply like
+
+00:59:38.400 --> 00:59:44.240
+this
+
+00:59:40.480 --> 00:59:47.880
+um or Matrix Vector multiply like this
+
+00:59:44.240 --> 00:59:50.200
+um and some of the rows are zero then uh
+
+00:59:47.880 --> 00:59:54.559
+that that's one place where it
+
+00:59:50.200 --> 00:59:58.200
+happens um you can also uh do this
+
+00:59:54.559 --> 01:00:00.119
+between zero and in not just rows but
+
+00:59:58.200 --> 01:00:02.200
+also larger
+
+01:00:00.119 --> 01:00:05.799
+tensors um and you can even do it in
+
+01:00:02.200 --> 01:00:07.599
+whole models in an ensemble so um the
+
+01:00:05.799 --> 01:00:10.799
+first one this can be optimized
+
+01:00:07.599 --> 01:00:13.880
+automatically by GPU um the second one
+
+01:00:10.799 --> 01:00:15.400
+this often occurs in uh sparse mixture
+
+01:00:13.880 --> 01:00:18.000
+of experts
+
+01:00:15.400 --> 01:00:19.400
+models and the final one uh basically
+
+01:00:18.000 --> 01:00:21.880
+you just don't need to even use the
+
+01:00:19.400 --> 01:00:24.119
+model in emble so if you somehow
+
+01:00:21.880 --> 01:00:25.640
+optimize an ensemble and it turns out
+
+01:00:24.119 --> 01:00:27.599
+that the probability of one of the
+
+01:00:25.640 --> 01:00:29.680
+models is zero you just can throw it out
+
+01:00:27.599 --> 01:00:33.640
+and not use it at
+
+01:00:29.680 --> 01:00:36.839
+all so um GPU level sparsity
+
+01:00:33.640 --> 01:00:39.839
+support uh Nvidia gpus support a bunch
+
+01:00:36.839 --> 01:00:42.559
+of different types of sparsity and uh
+
+01:00:39.839 --> 01:00:44.599
+the people the wonderful people at
+
+01:00:42.559 --> 01:00:48.280
+Nvidia have worked hard to make the
+
+01:00:44.599 --> 01:00:51.319
+support uh work to some extent anyway
+
+01:00:48.280 --> 01:00:53.119
+and uh there's a library called cpar and
+
+01:00:51.319 --> 01:00:56.119
+this is used in pytorch and all these
+
+01:00:53.119 --> 01:00:58.280
+other things as well and just to give
+
+01:00:56.119 --> 01:01:01.240
+example a vector Matrix multiply with a
+
+01:00:58.280 --> 01:01:03.240
+sparse Vector um such as one that comes
+
+01:01:01.240 --> 01:01:06.160
+from a relu activation basically what
+
+01:01:03.240 --> 01:01:09.319
+happens is let's say you only have three
+
+01:01:06.160 --> 01:01:11.799
+uh parts of this Vector that are active
+
+01:01:09.319 --> 01:01:15.240
+um you actually just don't need to cop
+
+01:01:11.799 --> 01:01:18.200
+uh calculate any of the columns here so
+
+01:01:15.240 --> 01:01:19.720
+that makes your life relatively
+
+01:01:18.200 --> 01:01:22.880
+easy
+
+01:01:19.720 --> 01:01:24.480
+um but the specific thing that I wanted
+
+01:01:22.880 --> 01:01:26.640
+to talk about is a sparsely gated
+
+01:01:24.480 --> 01:01:29.799
+mixture of experts layer because this is
+
+01:01:26.640 --> 01:01:33.960
+uh what is used in mixol and probably uh
+
+01:01:29.799 --> 01:01:38.200
+the GPT models as well and what you do
+
+01:01:33.960 --> 01:01:41.760
+is you have a feed forward Network and
+
+01:01:38.200 --> 01:01:41.760
+normally a feed forward Network in a
+
+01:01:43.640 --> 01:01:52.119
+Transformer is this like really wide
+
+01:01:49.319 --> 01:01:57.240
+thing this huge wide feed forward
+
+01:01:52.119 --> 01:01:59.359
+Network um that you use to extract a
+
+01:01:57.240 --> 01:02:00.520
+whole bunch of features at each layer
+
+01:01:59.359 --> 01:02:02.640
+and that's where a lot of the
+
+01:02:00.520 --> 01:02:05.799
+computation and Transformer
+
+01:02:02.640 --> 01:02:10.079
+happens um and what sparsely gated
+
+01:02:05.799 --> 01:02:13.079
+mixture of uh experts layers do is they
+
+01:02:10.079 --> 01:02:15.640
+first have this gating Network here
+
+01:02:13.079 --> 01:02:17.880
+where it calculates uh mixture
+
+01:02:15.640 --> 01:02:21.119
+probability but the mixture probability
+
+01:02:17.880 --> 01:02:23.039
+is zero and for many or most of the
+
+01:02:21.119 --> 01:02:26.880
+parts of this feed forward
+
+01:02:23.039 --> 01:02:28.760
+Network and so for the ones where it's
+
+01:02:26.880 --> 01:02:31.319
+zero you just don't calculate
+
+01:02:28.760 --> 01:02:34.319
+it um and then when you mix them
+
+01:02:31.319 --> 01:02:37.359
+together you use the mixture rates and
+
+01:02:34.319 --> 01:02:39.520
+this is actually really simple um it's
+
+01:02:37.359 --> 01:02:42.400
+like several lines of pytorch code maybe
+
+01:02:39.520 --> 01:02:45.319
+like seven or eight lines of P torch
+
+01:02:42.400 --> 01:02:48.720
+code but the basic uh idea here is you
+
+01:02:45.319 --> 01:02:50.599
+have um this gating function where you
+
+01:02:48.720 --> 01:02:52.799
+calculate the gating function based on
+
+01:02:50.599 --> 01:02:53.640
+the input and then you have this keep
+
+01:02:52.799 --> 01:02:56.720
+top
+
+01:02:53.640 --> 01:02:58.319
+K uh operation and then you take the
+
+01:02:56.720 --> 01:03:02.559
+soft Max over
+
+01:02:58.319 --> 01:03:04.359
+this and the keep top K operation is if
+
+01:03:02.559 --> 01:03:06.160
+the value is within the top K you just
+
+01:03:04.359 --> 01:03:07.319
+keep it and if it's not in the top K you
+
+01:03:06.160 --> 01:03:11.960
+don't keep
+
+01:03:07.319 --> 01:03:13.119
+it so that that's all basically but what
+
+01:03:11.960 --> 01:03:14.760
+what's great about this is then you
+
+01:03:13.119 --> 01:03:17.799
+don't have to calculate like many of
+
+01:03:14.760 --> 01:03:20.119
+them and so for example um uh if you
+
+01:03:17.799 --> 01:03:22.640
+keep the top two out of eight you reduce
+
+01:03:20.119 --> 01:03:26.760
+your calcul uh your computation by four
+
+01:03:22.640 --> 01:03:30.000
+times for this part so
+
+01:03:26.760 --> 01:03:33.000
+um any any questions
+
+01:03:30.000 --> 01:03:33.000
+here
+
+01:03:54.720 --> 01:03:57.720
+yeah
+
+01:04:03.160 --> 01:04:07.039
+um sorry what what exactly do you mean
+
+01:04:05.559 --> 01:04:09.400
+by easy to paralyze are you talking
+
+01:04:07.039 --> 01:04:12.400
+about like a GPU can calculate lots of
+
+01:04:09.400 --> 01:04:15.680
+things at the same time yeah so I think
+
+01:04:12.400 --> 01:04:17.720
+if you have a very small model um you're
+
+01:04:15.680 --> 01:04:21.680
+actually not going to get as much from
+
+01:04:17.720 --> 01:04:25.079
+this uh because you're not you're
+
+01:04:21.680 --> 01:04:26.359
+essentially not bound by computation uh
+
+01:04:25.079 --> 01:04:27.880
+like you're bound more by memory
+
+01:04:26.359 --> 01:04:29.079
+movement and the GPU and other stuff
+
+01:04:27.880 --> 01:04:30.520
+like that but once you start getting up
+
+01:04:29.079 --> 01:04:32.920
+to the bigger models you actually are
+
+01:04:30.520 --> 01:04:34.640
+bound by computation so reducing your
+
+01:04:32.920 --> 01:04:37.039
+computation by four actually is a big
+
+01:04:34.640 --> 01:04:42.559
+one so it's a really really good
+
+01:04:37.039 --> 01:04:42.559
+question um any any other questions
+
+01:04:44.039 --> 01:04:50.520
+yeah so so this will
+
+01:04:48.240 --> 01:04:53.160
+um probably
+
+01:04:50.520 --> 01:04:56.039
+be
+
+01:04:53.160 --> 01:04:59.279
+just oh sorry I I don't have this here
+
+01:04:56.039 --> 01:05:01.760
+but this will be a often a linear layer
+
+01:04:59.279 --> 01:05:01.760
+followed by a
+
+01:05:03.039 --> 01:05:08.000
+seance um or or actually no it doesn't
+
+01:05:06.359 --> 01:05:10.520
+even need to be followed by softb it
+
+01:05:08.000 --> 01:05:10.520
+could just be a
+
+01:05:12.520 --> 01:05:17.920
+linear and I think actually I didn't put
+
+01:05:14.960 --> 01:05:19.680
+it on this slide but I have the in the
+
+01:05:17.920 --> 01:05:21.359
+references on the website I have the
+
+01:05:19.680 --> 01:05:22.760
+actual implementation in mix roll you
+
+01:05:21.359 --> 01:05:25.279
+can go in and look at it it's really
+
+01:05:22.760 --> 01:05:27.160
+simple um one thing I didn't put on here
+
+01:05:25.279 --> 01:05:31.000
+um which actually uh relates to the
+
+01:05:27.160 --> 01:05:32.920
+question before is Hardware wise this
+
+01:05:31.000 --> 01:05:34.799
+implementation is tricky if you do
+
+01:05:32.920 --> 01:05:37.599
+batching um and the reason why It's
+
+01:05:34.799 --> 01:05:39.480
+Tricky if you do batching is because um
+
+01:05:37.599 --> 01:05:43.000
+different experts will be active for
+
+01:05:39.480 --> 01:05:45.240
+different like parts of the batch so if
+
+01:05:43.000 --> 01:05:48.559
+you do that you need to do some tricky
+
+01:05:45.240 --> 01:05:48.559
+stuff uh there's
+
+01:05:54.640 --> 01:05:57.640
+this
+
+01:06:03.240 --> 01:06:12.039
+like so much of AI research nowadays uh
+
+01:06:08.200 --> 01:06:12.039
+the best resource for this is social
+
+01:06:13.680 --> 01:06:20.000
+media so this is uh there's a kind of
+
+01:06:16.880 --> 01:06:23.240
+interesting discussion of
+
+01:06:20.000 --> 01:06:25.359
+this um if you search for like gpk Fast
+
+01:06:23.240 --> 01:06:28.400
+mixed r on Twitter it it talks about
+
+01:06:25.359 --> 01:06:30.200
+this but basically there's a bunch of uh
+
+01:06:28.400 --> 01:06:32.680
+little little things you need to pay
+
+01:06:30.200 --> 01:06:34.760
+attention to um and ways that you can do
+
+01:06:32.680 --> 01:06:36.960
+tricks to make this work fast on GPU
+
+01:06:34.760 --> 01:06:40.000
+which also kind of uh addresses the
+
+01:06:36.960 --> 01:06:42.359
+concern so you can look for Horus H's
+
+01:06:40.000 --> 01:06:44.200
+discussion
+
+01:06:42.359 --> 01:06:46.680
+this
+
+01:06:44.200 --> 01:06:49.000
+cool
+
+01:06:46.680 --> 01:06:50.799
+um so the final thing I'd like to talk
+
+01:06:49.000 --> 01:06:52.480
+about in the last 10 minutes is pipeline
+
+01:06:50.799 --> 01:06:55.359
+systems
+
+01:06:52.480 --> 01:06:57.039
+um and pipeline systems are systems
+
+01:06:55.359 --> 01:07:00.279
+where we
+
+01:06:57.039 --> 01:07:02.319
+have models that basically the output of
+
+01:07:00.279 --> 01:07:05.319
+one model becomes the input of another
+
+01:07:02.319 --> 01:07:05.319
+model
+
+01:07:05.599 --> 01:07:10.359
+and to give an example of this a
+
+01:07:08.200 --> 01:07:13.480
+cascaded system is basically a system
+
+01:07:10.359 --> 01:07:15.119
+like this where you uh take the output
+
+01:07:13.480 --> 01:07:16.960
+of one system and then you feed it into
+
+01:07:15.119 --> 01:07:19.640
+the input of another system so a very
+
+01:07:16.960 --> 01:07:22.880
+stereotypical example of This is speech
+
+01:07:19.640 --> 01:07:25.559
+translation um where you run speech and
+
+01:07:22.880 --> 01:07:27.720
+then you uh do speech recognition into
+
+01:07:25.559 --> 01:07:29.319
+text and then text you do machine
+
+01:07:27.720 --> 01:07:32.160
+translation into another
+
+01:07:29.319 --> 01:07:33.920
+language
+
+01:07:32.160 --> 01:07:36.440
+and
+
+01:07:33.920 --> 01:07:39.039
+um one of the frustrating things about
+
+01:07:36.440 --> 01:07:43.000
+speech translation is these systems are
+
+01:07:39.039 --> 01:07:45.799
+stubbornly better uh for a long time
+
+01:07:43.000 --> 01:07:47.680
+than many systems that try to do end to
+
+01:07:45.799 --> 01:07:49.960
+end like speech to text in another
+
+01:07:47.680 --> 01:07:52.160
+language there's a couple reasons for
+
+01:07:49.960 --> 01:07:54.440
+this does anyone have an idea why what
+
+01:07:52.160 --> 01:07:57.039
+one of those reasons might
+
+01:07:54.440 --> 01:07:58.839
+be
+
+01:07:57.039 --> 01:08:01.559
+yeah the
+
+01:07:58.839 --> 01:08:05.279
+data
+
+01:08:01.559 --> 01:08:08.680
+anying exactly so data data availability
+
+01:08:05.279 --> 01:08:10.920
+is way better for speech to text in the
+
+01:08:08.680 --> 01:08:13.319
+same language and text to text in
+
+01:08:10.920 --> 01:08:15.720
+another language than it is for uh
+
+01:08:13.319 --> 01:08:17.759
+Speech to te text in another language
+
+01:08:15.720 --> 01:08:19.319
+because there just aren't large data
+
+01:08:17.759 --> 01:08:21.679
+sets that have speech and text in many
+
+01:08:19.319 --> 01:08:25.719
+languages so there's a bunch of tricks
+
+01:08:21.679 --> 01:08:31.759
+that you can do uh to you know fix this
+
+01:08:25.719 --> 01:08:34.239
+but still it it's uh you know uh tricky
+
+01:08:31.759 --> 01:08:36.120
+and there's a couple other reasons
+
+01:08:34.239 --> 01:08:38.159
+another reason is like actually speech
+
+01:08:36.120 --> 01:08:39.319
+to text in the same language is just a
+
+01:08:38.159 --> 01:08:42.520
+much more
+
+01:08:39.319 --> 01:08:45.359
+straightforward task um and so it's a
+
+01:08:42.520 --> 01:08:47.839
+bit easier to learn another thing is
+
+01:08:45.359 --> 01:08:50.839
+interpretability and the reason why
+
+01:08:47.839 --> 01:08:52.120
+interpretability is important is
+
+01:08:50.839 --> 01:08:54.920
+basically
+
+01:08:52.120 --> 01:08:56.640
+like if I'm talking to you in a
+
+01:08:54.920 --> 01:08:58.000
+different language like you speak a
+
+01:08:56.640 --> 01:09:00.319
+different language I'm talking to you
+
+01:08:58.000 --> 01:09:02.679
+through a speech translation system I
+
+01:09:00.319 --> 01:09:05.799
+actually want to know if the speech
+
+01:09:02.679 --> 01:09:07.600
+recognition worked because I know if the
+
+01:09:05.799 --> 01:09:08.920
+speech recognition didn't work then I'll
+
+01:09:07.600 --> 01:09:10.440
+I'm pretty sure that the translation
+
+01:09:08.920 --> 01:09:11.920
+didn't work either right and I can
+
+01:09:10.440 --> 01:09:14.880
+verify the speech recognition but I
+
+01:09:11.920 --> 01:09:16.199
+can't verify the transation so um
+
+01:09:14.880 --> 01:09:18.279
+there's other reasons why you might want
+
+01:09:16.199 --> 01:09:20.239
+a Cascade system other than just like
+
+01:09:18.279 --> 01:09:22.440
+accuracy or or other things like that
+
+01:09:20.239 --> 01:09:25.880
+but this is a thing we definitely
+
+01:09:22.440 --> 01:09:29.120
+do um there's another idea of stacking
+
+01:09:25.880 --> 01:09:32.560
+and stacking is um very similar to cast
+
+01:09:29.120 --> 01:09:34.560
+skating but it allows you to take two
+
+01:09:32.560 --> 01:09:37.120
+different models for the same task but
+
+01:09:34.560 --> 01:09:39.400
+with predictions in different ways so
+
+01:09:37.120 --> 01:09:41.120
+just taking another um
+
+01:09:39.400 --> 01:09:43.600
+example
+
+01:09:41.120 --> 01:09:45.040
+uh actually maybe maybe ignore the
+
+01:09:43.600 --> 01:09:47.159
+example I have here but we could just
+
+01:09:45.040 --> 01:09:50.679
+take the example of speech uh
+
+01:09:47.159 --> 01:09:53.000
+translation um the speech translation
+
+01:09:50.679 --> 01:09:55.760
+model uh we would first do speech
+
+01:09:53.000 --> 01:09:57.520
+recognition into like let's say English
+
+01:09:55.760 --> 01:09:59.640
+and then we would do translation and the
+
+01:09:57.520 --> 01:10:03.840
+input to the translation model would be
+
+01:09:59.640 --> 01:10:05.560
+speech in English um text in English and
+
+01:10:03.840 --> 01:10:07.320
+we would generate the output in Japanese
+
+01:10:05.560 --> 01:10:10.080
+so it would take both the speech and the
+
+01:10:07.320 --> 01:10:12.920
+text uh when it was doing translation
+
+01:10:10.080 --> 01:10:14.840
+and that would allow it to number one
+
+01:10:12.920 --> 01:10:17.719
+basically get a second opinion about
+
+01:10:14.840 --> 01:10:21.080
+whether the transcription was correct
+
+01:10:17.719 --> 01:10:23.800
+but also like let's say there was
+
+01:10:21.080 --> 01:10:26.440
+some unique information that only
+
+01:10:23.800 --> 01:10:29.480
+appeared in the
+
+01:10:26.440 --> 01:10:31.679
+um uh that only appeared in the speech
+
+01:10:29.480 --> 01:10:34.840
+so just to give an example I read the
+
+01:10:31.679 --> 01:10:37.040
+book I read the book are both
+
+01:10:34.840 --> 01:10:38.640
+transcribed exactly the same way and
+
+01:10:37.040 --> 01:10:41.679
+they're different translations obviously
+
+01:10:38.640 --> 01:10:42.920
+because one is uh you know present or
+
+01:10:41.679 --> 01:10:45.560
+present tense and the other is past
+
+01:10:42.920 --> 01:10:47.239
+tense so there are examples where uh
+
+01:10:45.560 --> 01:10:51.600
+adding a cascaded system would lose
+
+01:10:47.239 --> 01:10:51.600
+information and a stacked system would
+
+01:10:53.400 --> 01:10:57.679
+not another thing is of refinement I
+
+01:10:56.440 --> 01:10:59.480
+think this is actually really
+
+01:10:57.679 --> 01:11:01.000
+interesting because large language
+
+01:10:59.480 --> 01:11:03.920
+models have opened up a whole bunch of
+
+01:11:01.000 --> 01:11:05.640
+possibilities for us in this space um
+
+01:11:03.920 --> 01:11:07.760
+this is like cascading and stacking but
+
+01:11:05.640 --> 01:11:09.640
+it it can be done multiple times and it
+
+01:11:07.760 --> 01:11:12.960
+can be done multiple times with the same
+
+01:11:09.640 --> 01:11:15.040
+model so um we have an input we feed it
+
+01:11:12.960 --> 01:11:17.320
+into the model we get an output and then
+
+01:11:15.040 --> 01:11:19.360
+we feed the output back in and gradually
+
+01:11:17.320 --> 01:11:23.080
+refine it and make it better and
+
+01:11:19.360 --> 01:11:24.760
+better and the first time this was done
+
+01:11:23.080 --> 01:11:27.440
+in neural networks was through something
+
+01:11:24.760 --> 01:11:29.679
+called Del ation networks and basically
+
+01:11:27.440 --> 01:11:32.360
+deliberation networks what they do is
+
+01:11:29.679 --> 01:11:33.760
+they uh take in an output and then they
+
+01:11:32.360 --> 01:11:34.920
+just gradually refine it to make it
+
+01:11:33.760 --> 01:11:37.280
+better and better they used a
+
+01:11:34.920 --> 01:11:39.159
+reinforcement learning algorithm to do
+
+01:11:37.280 --> 01:11:41.159
+this where you generated the output and
+
+01:11:39.159 --> 01:11:43.600
+then um improved
+
+01:11:41.159 --> 01:11:46.719
+it another thing that's really popular
+
+01:11:43.600 --> 01:11:48.280
+nowadays is uh diffusion models and I
+
+01:11:46.719 --> 01:11:50.400
+haven't quite decided whether I'll have
+
+01:11:48.280 --> 01:11:51.880
+time to cover diffusion models in depth
+
+01:11:50.400 --> 01:11:54.880
+but basically the way a diffusion model
+
+01:11:51.880 --> 01:11:55.880
+works is very similar you start out with
+
+01:11:54.880 --> 01:11:57.239
+nothing
+
+01:11:55.880 --> 01:11:59.840
+and then you gradually make it better
+
+01:11:57.239 --> 01:12:01.360
+and better um the key difference between
+
+01:11:59.840 --> 01:12:03.520
+deliberation networks and diffusion
+
+01:12:01.360 --> 01:12:05.520
+models is diffusion models um you can
+
+01:12:03.520 --> 01:12:08.600
+train from scratch by basically noising
+
+01:12:05.520 --> 01:12:10.600
+the input uh applying noise to the input
+
+01:12:08.600 --> 01:12:12.880
+um in training very efficiently and
+
+01:12:10.600 --> 01:12:15.639
+these are very widely used
+
+01:12:12.880 --> 01:12:18.199
+in image generation they're not super
+
+01:12:15.639 --> 01:12:20.120
+widely used in text just because regular
+
+01:12:18.199 --> 01:12:22.840
+autor regressive models are so good for
+
+01:12:20.120 --> 01:12:24.159
+text um but there are a few efforts to
+
+01:12:22.840 --> 01:12:26.880
+do
+
+01:12:24.159 --> 01:12:30.920
+that and then a final one is self-
+
+01:12:26.880 --> 01:12:35.120
+refine and the idea behind self- refine
+
+01:12:30.920 --> 01:12:39.400
+is you um actually maybe I can open the
+
+01:12:35.120 --> 01:12:39.400
+paper because the paper has a good
+
+01:12:54.120 --> 01:12:58.239
+figure
+
+01:12:56.280 --> 01:13:02.679
+actually I thought it had a good
+
+01:12:58.239 --> 01:13:05.600
+figure um yeah so maybe this is a figure
+
+01:13:02.679 --> 01:13:08.639
+um so basically uh what you do is you
+
+01:13:05.600 --> 01:13:10.639
+feed in the input you generate an output
+
+01:13:08.639 --> 01:13:12.679
+and then you ask the model to give you
+
+01:13:10.639 --> 01:13:15.520
+feedback on the output and say yes this
+
+01:13:12.679 --> 01:13:16.760
+output is good or um like let's say
+
+01:13:15.520 --> 01:13:19.679
+you're doing code generation it could
+
+01:13:16.760 --> 01:13:21.920
+say no this output has an error in it um
+
+01:13:19.679 --> 01:13:24.719
+this is a problem with your output and
+
+01:13:21.920 --> 01:13:27.840
+then you feed in both the output and the
+
+01:13:24.719 --> 01:13:29.480
+feedback back uh and ask the model to
+
+01:13:27.840 --> 01:13:32.239
+refine its output and you do this over
+
+01:13:29.480 --> 01:13:35.280
+and over again and this allows you to uh
+
+01:13:32.239 --> 01:13:36.840
+improve the output and uh this is has
+
+01:13:35.280 --> 01:13:39.600
+ended up being pretty effective in a
+
+01:13:36.840 --> 01:13:41.159
+pretty wide number of tasks one caveat
+
+01:13:39.600 --> 01:13:44.040
+about this is your model has to be
+
+01:13:41.159 --> 01:13:47.000
+really good for this to work so um only
+
+01:13:44.040 --> 01:13:49.239
+models kind of on the level of GPT 4 not
+
+01:13:47.000 --> 01:13:52.000
+on the level of GPT 3.5 have the ability
+
+01:13:49.239 --> 01:13:54.040
+to do this pretty consistently so it is
+
+01:13:52.000 --> 01:13:57.040
+something you need to be aware
+
+01:13:54.040 --> 01:13:57.040
+of
+
+01:13:59.760 --> 01:14:03.600
+cool yep that's all I I had for today
+
+01:14:02.400 --> 01:14:06.600
+I'm happy
+
+01:14:03.600 --> 01:14:06.600
+to
+
+01:14:07.159 --> 01:14:10.159
+take
+
+01:14:20.600 --> 01:14:27.320
+yep yep that this is a great question so
+
+01:14:23.920 --> 01:14:28.840
+if sta has the potential to address
+
+01:14:27.320 --> 01:14:32.120
+information loss why would we ever
+
+01:14:28.840 --> 01:14:33.840
+choose a Cascade model I think basically
+
+01:14:32.120 --> 01:14:37.440
+there's potentially two reasons one
+
+01:14:33.840 --> 01:14:39.199
+reason is um data availability so in
+
+01:14:37.440 --> 01:14:42.639
+order to train a stacked model you
+
+01:14:39.199 --> 01:14:43.430
+obviously need the outputs I guess you
+
+01:14:42.639 --> 01:14:44.639
+could
+
+01:14:43.430 --> 01:14:48.440
+[Music]
+
+01:14:44.639 --> 01:14:50.880
+um yeah I guess you could run
+
+01:14:48.440 --> 01:14:53.199
+the and generate outputs for every
+
+01:14:50.880 --> 01:14:54.840
+training example you have um but you
+
+01:14:53.199 --> 01:14:55.840
+would need to do that so you would need
+
+01:14:54.840 --> 01:14:58.639
+to to
+
+01:14:55.840 --> 01:14:59.920
+run speech recognition for every example
+
+01:14:58.639 --> 01:15:02.760
+and you also
+
+01:14:59.920 --> 01:15:05.199
+couldn't you couldn't use any examples
+
+01:15:02.760 --> 01:15:07.600
+where you don't have the original input
+
+01:15:05.199 --> 01:15:10.320
+so you couldn't use text to text
+
+01:15:07.600 --> 01:15:12.239
+examples unless you like synthesize
+
+01:15:10.320 --> 01:15:14.159
+speech from text for machine translation
+
+01:15:12.239 --> 01:15:15.840
+for example so makes it a little bit
+
+01:15:14.159 --> 01:15:17.360
+more tricky due to the data requirements
+
+01:15:15.840 --> 01:15:19.239
+but that's not
+
+01:15:17.360 --> 01:15:22.560
+insurmountable the second reason is
+
+01:15:19.239 --> 01:15:24.400
+complexity and efficiency so you know
+
+01:15:22.560 --> 01:15:27.920
+you do have to come up with a model that
+
+01:15:24.400 --> 01:15:29.520
+takes in speed and text and run set and
+
+01:15:27.920 --> 01:15:30.920
+it might be easier just to hook together
+
+01:15:29.520 --> 01:15:34.719
+a speech recognitional with a
+
+01:15:30.920 --> 01:15:37.920
+translation so but like I think overall
+
+01:15:34.719 --> 01:15:39.639
+I I like these methods I I think these
+
+01:15:37.920 --> 01:15:41.159
+are good methods to use if you're if
+
+01:15:39.639 --> 01:15:42.480
+you're thinking about using a Cascade
+
+01:15:41.159 --> 01:15:44.199
+system you should definitely consider
+
+01:15:42.480 --> 01:15:47.199
+using a stack system in
+
+01:15:44.199 --> 01:15:47.199
+sense
+
+01:15:52.080 --> 01:15:56.960
+yeah yeah can you measure the
+
+01:15:55.159 --> 01:15:59.400
+contribution of each component to an
+
+01:15:56.960 --> 01:16:00.639
+ensemble um the very very easy way to do
+
+01:15:59.400 --> 01:16:02.199
+that is look at the interpolation
+
+01:16:00.639 --> 01:16:05.360
+coefficients if you train the
+
+01:16:02.199 --> 01:16:06.800
+interpolation coefficients um otherwise
+
+01:16:05.360 --> 01:16:08.920
+I guess it depends on what you mean by
+
+01:16:06.800 --> 01:16:10.480
+each contribution but I you know looking
+
+01:16:08.920 --> 01:16:12.280
+at the interpolation coefficients is a
+
+01:16:10.480 --> 01:16:16.320
+pretty good way to do
+
+01:16:12.280 --> 01:16:16.320
+it also just how much did the
+
+01:16:21.480 --> 01:16:27.400
+accuracy is iterative refinement the
+
+01:16:24.159 --> 01:16:30.199
+same idea as boosting in traditional
+
+01:16:27.400 --> 01:16:30.199
+like machine Learning
+
+01:16:30.320 --> 01:16:34.920
+Systems I think it's a little bit
+
+01:16:32.920 --> 01:16:36.520
+different um because iterative
+
+01:16:34.920 --> 01:16:38.920
+refinement what I'm talking about here
+
+01:16:36.520 --> 01:16:41.120
+it's usually taking in the output like
+
+01:16:38.920 --> 01:16:43.320
+rather complex output of a system and
+
+01:16:41.120 --> 01:16:44.920
+modifying it so you're not just
+
+01:16:43.320 --> 01:16:47.080
+modifying the
+
+01:16:44.920 --> 01:16:49.880
+probabilities of like a single
+
+01:16:47.080 --> 01:16:53.080
+classifier you're modifying the actual
+
+01:16:49.880 --> 01:16:55.960
+outputs that were generated then from
+
+01:16:53.080 --> 01:16:59.560
+the point of view of a boosting
+
+01:16:55.960 --> 01:17:02.560
+model over a single categorical output
+
+01:16:59.560 --> 01:17:04.520
+it might actually be similar or the same
+
+01:17:02.560 --> 01:17:06.480
+but this is more like uh you you
+
+01:17:04.520 --> 01:17:08.159
+generated a textual output and then you
+
+01:17:06.480 --> 01:17:10.400
+feed in the textual output to the other
+
+01:17:08.159 --> 01:17:12.120
+model and refine like generated a new
+
+01:17:10.400 --> 01:17:14.239
+textual output so I feel like it's a lot
+
+01:17:12.120 --> 01:17:18.639
+more
+
+01:17:14.239 --> 01:17:18.639
+complex cool okay thank thanks a lot
+
+01:17:18.840 --> 01:17:21.840
+everyone