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+WEBVTT
+
+00:00:00.120 --> 00:00:04.880
+everyone I today I'd like to talk about
+
+00:00:02.760 --> 00:00:07.399
+uh learning from knowledge bases uh
+
+00:00:04.880 --> 00:00:11.440
+learning from in for knowledge bases
+
+00:00:07.399 --> 00:00:14.799
+this is kind of a a shift uh from a lot
+
+00:00:11.440 --> 00:00:16.480
+of the stuff that we've done so far uh
+
+00:00:14.799 --> 00:00:18.439
+and I'm going to be talking about like a
+
+00:00:16.480 --> 00:00:20.480
+different information Source some
+
+00:00:18.439 --> 00:00:21.960
+relatively different algorithms compared
+
+00:00:20.480 --> 00:00:26.080
+to the stuff that we talked about up
+
+00:00:21.960 --> 00:00:28.880
+until this point so um you know it might
+
+00:00:26.080 --> 00:00:32.360
+be uh interesting it might be different
+
+00:00:28.880 --> 00:00:35.640
+so uh get started with
+
+00:00:32.360 --> 00:00:37.360
+that so I'm going to be talking about
+
+00:00:35.640 --> 00:00:40.000
+knowledge bases and knowledge bases are
+
+00:00:37.360 --> 00:00:43.039
+basically a structured databases of
+
+00:00:40.000 --> 00:00:46.079
+knowledge and they can contain a lot of
+
+00:00:43.039 --> 00:00:48.559
+things but most commonly when people are
+
+00:00:46.079 --> 00:00:50.600
+talking about them they are talking
+
+00:00:48.559 --> 00:00:53.160
+about relational knowledge bases that
+
+00:00:50.600 --> 00:00:55.559
+include things like entities which are
+
+00:00:53.160 --> 00:00:57.399
+nodes in a graph and relations which are
+
+00:00:55.559 --> 00:01:00.239
+edges between
+
+00:00:57.399 --> 00:01:02.079
+nodes and
+
+00:01:00.239 --> 00:01:03.879
+I'll I'll talk about some examples of
+
+00:01:02.079 --> 00:01:05.479
+this in a little bit to make that a
+
+00:01:03.879 --> 00:01:08.040
+little bit more concrete and then some
+
+00:01:05.479 --> 00:01:11.240
+of the questions that we ask about these
+
+00:01:08.040 --> 00:01:14.400
+are how can we learn to create and
+
+00:01:11.240 --> 00:01:16.799
+expand knowledge bases with uh you know
+
+00:01:14.400 --> 00:01:18.439
+neural network based methods and then
+
+00:01:16.799 --> 00:01:20.200
+the second question is how can we learn
+
+00:01:18.439 --> 00:01:22.600
+from the information in knowledge bases
+
+00:01:20.200 --> 00:01:24.720
+to improve like neural network models or
+
+00:01:22.600 --> 00:01:27.560
+uh use them in effective
+
+00:01:24.720 --> 00:01:31.479
+ways and how can we use uh structured
+
+00:01:27.560 --> 00:01:31.479
+knowledge to answer questions
+
+00:01:32.200 --> 00:01:37.159
+so the first uh thing I'd like to talk
+
+00:01:35.000 --> 00:01:40.960
+about a little bit is types of knowledge
+
+00:01:37.159 --> 00:01:43.079
+bases and they come in several different
+
+00:01:40.960 --> 00:01:46.119
+varieties the first one I'd like to talk
+
+00:01:43.079 --> 00:01:48.560
+about is a very uh classical one called
+
+00:01:46.119 --> 00:01:50.960
+wordnet has anyone actually ever used
+
+00:01:48.560 --> 00:01:53.479
+wordnet
+
+00:01:50.960 --> 00:01:55.520
+before I see at least one person raising
+
+00:01:53.479 --> 00:01:57.640
+their hand so it's not entirely uh
+
+00:01:55.520 --> 00:02:00.119
+hasn't entirely disappeared has anyone
+
+00:01:57.640 --> 00:02:03.240
+heard of wordnet before
+
+00:02:00.119 --> 00:02:05.079
+okay more more people um so basically
+
+00:02:03.240 --> 00:02:06.960
+this used to be a really big thing in in
+
+00:02:05.079 --> 00:02:10.440
+natural language processing it's not So
+
+00:02:06.960 --> 00:02:12.319
+Much Anymore um but I I want to explain
+
+00:02:10.440 --> 00:02:14.800
+about it because I want to explain why
+
+00:02:12.319 --> 00:02:17.360
+this is maybe like less necessary to use
+
+00:02:14.800 --> 00:02:19.599
+but actual knowledge bases are still
+
+00:02:17.360 --> 00:02:23.160
+more necessary to
+
+00:02:19.599 --> 00:02:26.280
+use and so wordnet is a large database
+
+00:02:23.160 --> 00:02:29.560
+of words and specifically what it does
+
+00:02:26.280 --> 00:02:32.720
+is each word or something they call a
+
+00:02:29.560 --> 00:02:37.120
+syn set is a node and then there are
+
+00:02:32.720 --> 00:02:42.560
+relationships between nodes and the
+
+00:02:37.120 --> 00:02:44.319
+nodes can correspond to nouns um and or
+
+00:02:42.560 --> 00:02:45.920
+verbs or
+
+00:02:44.319 --> 00:02:48.360
+adjectives
+
+00:02:45.920 --> 00:02:49.959
+and nouns have different types of
+
+00:02:48.360 --> 00:02:53.360
+relations between them so they have
+
+00:02:49.959 --> 00:02:56.280
+things like an is a relation so like a
+
+00:02:53.360 --> 00:03:00.040
+hatchback is a type of car they are part
+
+00:02:56.280 --> 00:03:02.840
+of relations uh where a wheel is a part
+
+00:03:00.040 --> 00:03:05.720
+of a car um and they also make
+
+00:03:02.840 --> 00:03:09.799
+distinctions between types and instances
+
+00:03:05.720 --> 00:03:12.400
+so like Joe Biden is an instance of a
+
+00:03:09.799 --> 00:03:16.560
+president and president is the
+
+00:03:12.400 --> 00:03:19.239
+type so um verb relations are ordered by
+
+00:03:16.560 --> 00:03:22.680
+specificity so like communicate is more
+
+00:03:19.239 --> 00:03:25.799
+broad than talk so talk is you know
+
+00:03:22.680 --> 00:03:27.519
+generally a sub class of communicate and
+
+00:03:25.799 --> 00:03:30.720
+then whisper is generally a subass of
+
+00:03:27.519 --> 00:03:33.159
+talk so it's ordered in this way
+
+00:03:30.720 --> 00:03:35.920
+and then adjective relations are mostly
+
+00:03:33.159 --> 00:03:37.720
+antonyms so like wet and wet versus dry
+
+00:03:35.920 --> 00:03:43.599
+and other things like
+
+00:03:37.720 --> 00:03:47.080
+this um when I said sinets uh actually
+
+00:03:43.599 --> 00:03:50.239
+the each node is not a word despite the
+
+00:03:47.080 --> 00:03:53.239
+name word net it's a set of words that
+
+00:03:50.239 --> 00:03:56.200
+all have the same meaning so you might
+
+00:03:53.239 --> 00:03:59.120
+have artifact and thing would both
+
+00:03:56.200 --> 00:04:00.879
+correspond to this um node because they
+
+00:03:59.120 --> 00:04:02.599
+both mean basically the same thing so
+
+00:04:00.879 --> 00:04:04.159
+it's like sets of synonyms and this is
+
+00:04:02.599 --> 00:04:07.599
+also important when we talk about other
+
+00:04:04.159 --> 00:04:09.920
+types of uh knowledge bases as well and
+
+00:04:07.599 --> 00:04:13.920
+so what was this used for um this was
+
+00:04:09.920 --> 00:04:17.160
+used for for example uh trying to figure
+
+00:04:13.920 --> 00:04:22.400
+out whether trying to find all the cars
+
+00:04:17.160 --> 00:04:24.440
+that were mentioned in like a in a large
+
+00:04:22.400 --> 00:04:27.440
+set of text so you would go through you
+
+00:04:24.440 --> 00:04:30.280
+would identify all
+
+00:04:27.440 --> 00:04:32.120
+sinets or you would identify all words
+
+00:04:30.280 --> 00:04:34.120
+that corresponded to these sunsets and
+
+00:04:32.120 --> 00:04:35.720
+then you would take a step up and find
+
+00:04:34.120 --> 00:04:38.800
+motor car and you would know that like
+
+00:04:35.720 --> 00:04:42.320
+all of those were mentions of cars so
+
+00:04:38.800 --> 00:04:45.520
+like why don't we use wordnet very much
+
+00:04:42.320 --> 00:04:45.520
+anymore any
+
+00:04:49.160 --> 00:04:52.840
+ideas what would what would you do
+
+00:04:51.080 --> 00:04:55.560
+instead if I told you find all the cars
+
+00:04:52.840 --> 00:04:55.560
+in a big piece of
+
+00:04:55.960 --> 00:05:00.160
+text yeah just do something with the
+
+00:04:58.280 --> 00:05:02.880
+embeding just do something with
+
+00:05:00.160 --> 00:05:04.560
+embeddings yeah so you might get um you
+
+00:05:02.880 --> 00:05:06.720
+might get something and find all things
+
+00:05:04.560 --> 00:05:10.360
+that were close in embedding space to a
+
+00:05:06.720 --> 00:05:10.360
+car what what's another thing you might
+
+00:05:11.560 --> 00:05:15.520
+do like what I would do is I would
+
+00:05:13.639 --> 00:05:17.080
+download mistol and say does this
+
+00:05:15.520 --> 00:05:19.880
+sentence talk about a car and it would
+
+00:05:17.080 --> 00:05:22.199
+say yes or no and I I would you know or
+
+00:05:19.880 --> 00:05:23.479
+I would say find all the cars in this uh
+
+00:05:22.199 --> 00:05:25.319
+that are mentioned in the sentence and
+
+00:05:23.479 --> 00:05:28.720
+it would get them and sure that's like
+
+00:05:25.319 --> 00:05:31.319
+expensive but it's really easy so um you
+
+00:05:28.720 --> 00:05:32.919
+know there are other options that might
+
+00:05:31.319 --> 00:05:36.720
+be less expensive but that could solve a
+
+00:05:32.919 --> 00:05:39.520
+lot of the things so word not you know
+
+00:05:36.720 --> 00:05:41.039
+started out with more and more it it
+
+00:05:39.520 --> 00:05:42.600
+started out being very popular in
+
+00:05:41.039 --> 00:05:44.039
+natural language processing but now it's
+
+00:05:42.600 --> 00:05:45.440
+less so because we can get a lot of it
+
+00:05:44.039 --> 00:05:47.639
+from embeddings we can get a lot of it
+
+00:05:45.440 --> 00:05:50.520
+from language models
+
+00:05:47.639 --> 00:05:52.759
+itself um another thing that started
+
+00:05:50.520 --> 00:05:55.759
+maybe before wordnet or even around the
+
+00:05:52.759 --> 00:05:58.840
+same time as wordnet was this uh data
+
+00:05:55.759 --> 00:06:00.800
+base called psych and it was a manually
+
+00:05:58.840 --> 00:06:04.160
+curated database attempting to encode
+
+00:06:00.800 --> 00:06:06.280
+all common sense knowledge um and the
+
+00:06:04.160 --> 00:06:08.759
+project itself lasted for about 30 to 40
+
+00:06:06.280 --> 00:06:11.840
+years it might even still
+
+00:06:08.759 --> 00:06:13.319
+exist um and so they had this huge uh
+
+00:06:11.840 --> 00:06:15.199
+like hierarchy of all the different
+
+00:06:13.319 --> 00:06:17.680
+types of knowledge you could have it
+
+00:06:15.199 --> 00:06:19.680
+encoded knowledge about like events and
+
+00:06:17.680 --> 00:06:21.479
+like which events happened before other
+
+00:06:19.680 --> 00:06:26.840
+events and all these other stuff like
+
+00:06:21.479 --> 00:06:29.039
+this um but the problem with this is uh
+
+00:06:26.840 --> 00:06:31.000
+this was just too ambitious basically it
+
+00:06:29.039 --> 00:06:35.680
+was not possible to encode all of this
+
+00:06:31.000 --> 00:06:37.440
+manually by hand so people um like it it
+
+00:06:35.680 --> 00:06:38.840
+did it got part of the way there but
+
+00:06:37.440 --> 00:06:40.240
+that part of the way there was not
+
+00:06:38.840 --> 00:06:42.560
+enough for it to be really useful in
+
+00:06:40.240 --> 00:06:45.199
+Practical systems so it isn't this sort
+
+00:06:42.560 --> 00:06:47.800
+of method is not used as frequently
+
+00:06:45.199 --> 00:06:51.240
+now
+
+00:06:47.800 --> 00:06:56.000
+um a a followup one
+
+00:06:51.240 --> 00:06:57.479
+um which is it's successor is now uh the
+
+00:06:56.000 --> 00:06:59.879
+the most widely used knowledge Bas is
+
+00:06:57.479 --> 00:07:03.240
+something called dbpedia and the basic
+
+00:06:59.879 --> 00:07:06.120
+idea behind dbpedia is that while Psych
+
+00:07:03.240 --> 00:07:07.840
+is too difficult because they had people
+
+00:07:06.120 --> 00:07:12.400
+on the psych project who would go in and
+
+00:07:07.840 --> 00:07:12.400
+curate rules um for
+
+00:07:13.280 --> 00:07:19.080
+machines Wikipedia basically they have a
+
+00:07:17.160 --> 00:07:21.080
+very very large number of humans
+
+00:07:19.080 --> 00:07:23.639
+curating this structured data about
+
+00:07:21.080 --> 00:07:25.199
+entities in the world for humans they're
+
+00:07:23.639 --> 00:07:27.879
+creating it for humans because then you
+
+00:07:25.199 --> 00:07:29.599
+can put it on a Wikipedia page and you
+
+00:07:27.879 --> 00:07:31.440
+can look and see it says cardig melan
+
+00:07:29.599 --> 00:07:34.160
+University it has the former names of
+
+00:07:31.440 --> 00:07:36.919
+Carnegie melon um it has the motto of
+
+00:07:34.160 --> 00:07:38.759
+Carnegie melon the type of entity who it
+
+00:07:36.919 --> 00:07:41.360
+was established by and when and other
+
+00:07:38.759 --> 00:07:42.840
+stuff like that and because people are
+
+00:07:41.360 --> 00:07:44.280
+no longer creating it for machines
+
+00:07:42.840 --> 00:07:46.280
+they're creating it for humans people
+
+00:07:44.280 --> 00:07:47.840
+are like motivated to do this so like
+
+00:07:46.280 --> 00:07:49.960
+lots of people will do it for free so
+
+00:07:47.840 --> 00:07:51.960
+you can actually get a reasonably sized
+
+00:07:49.960 --> 00:07:53.639
+amount of data from this and actually
+
+00:07:51.960 --> 00:07:55.720
+cover you know like most of the entities
+
+00:07:53.639 --> 00:07:57.080
+in the world or not most of the entities
+
+00:07:55.720 --> 00:08:00.120
+in the world but most of the notable
+
+00:07:57.080 --> 00:08:03.319
+entities in uh part of the world that
+
+00:08:00.120 --> 00:08:03.319
+have high participation in
+
+00:08:03.479 --> 00:08:09.800
+Wikipedia um so now the the thing that a
+
+00:08:08.039 --> 00:08:13.319
+lot of people use is something called
+
+00:08:09.800 --> 00:08:14.919
+Wiki data this is not this name is a
+
+00:08:13.319 --> 00:08:17.039
+little bit of a misnomer because it's
+
+00:08:14.919 --> 00:08:18.960
+not actually that closely connected to
+
+00:08:17.039 --> 00:08:20.639
+Wikipedia they extract data from
+
+00:08:18.960 --> 00:08:21.720
+Wikipedia but they also extract it from
+
+00:08:20.639 --> 00:08:24.400
+lots of other
+
+00:08:21.720 --> 00:08:27.520
+sources and this is a curated database
+
+00:08:24.400 --> 00:08:30.360
+of entities um it's linked it's
+
+00:08:27.520 --> 00:08:33.959
+extremely large scale and it's
+
+00:08:30.360 --> 00:08:38.080
+multilingual and um this is an example
+
+00:08:33.959 --> 00:08:39.680
+of a thing from Richard fean um where
+
+00:08:38.080 --> 00:08:42.680
+people can go in and they can actually
+
+00:08:39.680 --> 00:08:45.320
+like add information and stuff like that
+
+00:08:42.680 --> 00:08:47.440
+um and you know it gives information
+
+00:08:45.320 --> 00:08:50.959
+about education and all kinds of other
+
+00:08:47.440 --> 00:08:52.600
+stuff so um for fun I can go to the wiki
+
+00:08:50.959 --> 00:08:55.040
+data
+
+00:08:52.600 --> 00:08:59.360
+site does anyone have an entity they'd
+
+00:08:55.040 --> 00:08:59.360
+like to know more about
+
+00:09:01.640 --> 00:09:07.320
+any any ideas maybe something that has
+
+00:09:03.959 --> 00:09:07.320
+been in the news recently
+
+00:09:10.680 --> 00:09:16.160
+or nobody brave enough to come up with
+
+00:09:13.040 --> 00:09:18.360
+an entity yeah
+
+00:09:16.160 --> 00:09:20.640
+Mamba that's a good one I'm actually not
+
+00:09:18.360 --> 00:09:23.800
+sure if that one's going to be in here
+
+00:09:20.640 --> 00:09:27.720
+um there's lots of mambas but I don't
+
+00:09:23.800 --> 00:09:27.720
+know about that particular Mamba let me
+
+00:09:27.839 --> 00:09:31.200
+see do you want to know about a
+
+00:09:29.720 --> 00:09:33.399
+different Mamba do you want about know
+
+00:09:31.200 --> 00:09:36.040
+about Mamba the research
+
+00:09:33.399 --> 00:09:38.399
+group so Mamba is a research group it's
+
+00:09:36.040 --> 00:09:41.800
+the modeling and Analysis for medicine
+
+00:09:38.399 --> 00:09:44.800
+research group um it focuses on
+
+00:09:41.800 --> 00:09:48.000
+mathematical biology and it's in the uh
+
+00:09:44.800 --> 00:09:51.120
+in this National Center for scientific
+
+00:09:48.000 --> 00:09:52.519
+research in France um the chairperson is
+
+00:09:51.120 --> 00:09:55.360
+this person and stuff like that so you
+
+00:09:52.519 --> 00:10:00.200
+can see it has all of these things so
+
+00:09:55.360 --> 00:10:03.920
+Mamba this Mamba is a node in the graph
+
+00:10:00.200 --> 00:10:06.839
+and then the edges are pointing um the
+
+00:10:03.920 --> 00:10:09.440
+edges are labeled with like instance of
+
+00:10:06.839 --> 00:10:11.200
+and then the next note is research group
+
+00:10:09.440 --> 00:10:13.000
+so research group is like another note
+
+00:10:11.200 --> 00:10:17.120
+in the graph and so you can click
+
+00:10:13.000 --> 00:10:18.680
+through this and it has its own ID and
+
+00:10:17.120 --> 00:10:21.200
+other things like
+
+00:10:18.680 --> 00:10:22.839
+this also you'll notice that research
+
+00:10:21.200 --> 00:10:24.160
+group is translated into lots of
+
+00:10:22.839 --> 00:10:27.440
+different languages in the world so you
+
+00:10:24.160 --> 00:10:30.120
+can use it multi multilingually and um
+
+00:10:27.440 --> 00:10:33.880
+and other things like that
+
+00:10:30.120 --> 00:10:37.000
+um even minor entities like Graham
+
+00:10:33.880 --> 00:10:40.160
+nuig are included in this and it has a
+
+00:10:37.000 --> 00:10:42.240
+little bit of um like information about
+
+00:10:40.160 --> 00:10:45.480
+me like my PhD was in Kyoto University
+
+00:10:42.240 --> 00:10:45.480
+in 2012 I am a
+
+00:10:45.600 --> 00:10:52.079
+human I I am male uh and first name last
+
+00:10:50.519 --> 00:10:53.720
+name University teacher computer
+
+00:10:52.079 --> 00:10:56.279
+scientist natural language processing
+
+00:10:53.720 --> 00:10:58.639
+this is all right um because this is
+
+00:10:56.279 --> 00:11:00.240
+mostly hand curated it even has the IDS
+
+00:10:58.639 --> 00:11:04.240
+of my advisor
+
+00:11:00.240 --> 00:11:06.519
+advisers um the reason why it has all of
+
+00:11:04.240 --> 00:11:09.839
+this stuff actually is because like 15
+
+00:11:06.519 --> 00:11:12.160
+years ago or like 10 years ago I entered
+
+00:11:09.839 --> 00:11:14.399
+in my uh my information into the
+
+00:11:12.160 --> 00:11:16.240
+mathematical genealogy project uh which
+
+00:11:14.399 --> 00:11:18.880
+is this project about who your advisers
+
+00:11:16.240 --> 00:11:20.680
+were because I wanted to see like who my
+
+00:11:18.880 --> 00:11:22.800
+mathematical like siblings were and
+
+00:11:20.680 --> 00:11:24.519
+stuff like that and uh somehow they
+
+00:11:22.800 --> 00:11:27.360
+managed to pull that out and keep this
+
+00:11:24.519 --> 00:11:28.760
+like 10 years later so um basically
+
+00:11:27.360 --> 00:11:30.519
+they're pulling information from like
+
+00:11:28.760 --> 00:11:32.800
+many many different structured data
+
+00:11:30.519 --> 00:11:34.160
+sources that they can use so uh they can
+
+00:11:32.800 --> 00:11:37.480
+pull it in there I don't know where they
+
+00:11:34.160 --> 00:11:39.440
+got that I'm human uh but maybe that was
+
+00:11:37.480 --> 00:11:43.240
+inferred from some piece of data
+
+00:11:39.440 --> 00:11:44.760
+somewhere online or something cool um
+
+00:11:43.240 --> 00:11:46.839
+another good thing about this that
+
+00:11:44.760 --> 00:11:52.680
+actually I didn't mention directly in
+
+00:11:46.839 --> 00:11:52.680
+the um in the lecture note or
+
+00:11:54.680 --> 00:12:01.120
+slides is that there's a query language
+
+00:11:57.360 --> 00:12:04.320
+for this yeah and a query language this
+
+00:12:01.120 --> 00:12:06.839
+query language is called Sparkle so
+
+00:12:04.320 --> 00:12:10.680
+there's a sequel for querying relational
+
+00:12:06.839 --> 00:12:14.399
+databases and Sparkle is for querying
+
+00:12:10.680 --> 00:12:15.240
+these uh knowledge bases and let me see
+
+00:12:14.399 --> 00:12:18.279
+if I
+
+00:12:15.240 --> 00:12:22.560
+can I asked chat
+
+00:12:18.279 --> 00:12:24.560
+GPT to write me a sparkle query to find
+
+00:12:22.560 --> 00:12:26.839
+all presidents of Carnegie melon
+
+00:12:24.560 --> 00:12:31.160
+University so let's see if Chad GPT is
+
+00:12:26.839 --> 00:12:31.160
+capable of doing that um
+
+00:12:35.639 --> 00:12:39.680
+okay that's a problem let me
+
+00:12:41.279 --> 00:12:47.000
+see okay there's there's an errand there
+
+00:12:43.880 --> 00:12:48.360
+but like if uh uh if I could find a I
+
+00:12:47.000 --> 00:12:50.160
+don't want to waste time in class like
+
+00:12:48.360 --> 00:12:52.079
+finding a working query but basically
+
+00:12:50.160 --> 00:12:53.399
+you can put it in a query and it allows
+
+00:12:52.079 --> 00:12:56.120
+you to do a lot of things that are
+
+00:12:53.399 --> 00:13:00.519
+similar to what you can do in SQL so you
+
+00:12:56.120 --> 00:13:02.720
+can find like all of the edges of nodes
+
+00:13:00.519 --> 00:13:05.279
+that satisfy a particular relation so
+
+00:13:02.720 --> 00:13:07.360
+you could say I want for Carnegie melon
+
+00:13:05.279 --> 00:13:10.160
+University to find all things that
+
+00:13:07.360 --> 00:13:13.519
+followed the like president of relation
+
+00:13:10.160 --> 00:13:14.959
+and that would give me all um you know
+
+00:13:13.519 --> 00:13:18.680
+all presidents of Carnegie melon
+
+00:13:14.959 --> 00:13:20.440
+University you can also like filter um
+
+00:13:18.680 --> 00:13:22.160
+filter by their start date and end date
+
+00:13:20.440 --> 00:13:24.120
+so find all of the preceden between a
+
+00:13:22.160 --> 00:13:25.839
+certain time and a another time or
+
+00:13:24.120 --> 00:13:30.480
+things like
+
+00:13:25.839 --> 00:13:34.199
+that so this is good if you want to get
+
+00:13:30.480 --> 00:13:36.600
+like high reli high reliability data um
+
+00:13:34.199 --> 00:13:39.839
+in a scalable way because like if I ask
+
+00:13:36.600 --> 00:13:41.920
+chat GPT like one of my favorite um one
+
+00:13:39.839 --> 00:13:45.720
+of my favorite queries for chat GPT is
+
+00:13:41.920 --> 00:13:48.600
+like name all of the name all of the
+
+00:13:45.720 --> 00:13:51.959
+presidents that were born uh east of the
+
+00:13:48.600 --> 00:13:53.880
+Mississippi River um and I've never
+
+00:13:51.959 --> 00:13:56.519
+successfully gotten chat GPT to be able
+
+00:13:53.880 --> 00:13:57.800
+to do this um because there's lots of
+
+00:13:56.519 --> 00:13:59.560
+presidents who were born east of the
+
+00:13:57.800 --> 00:14:02.320
+Mississippi River and it starts counting
+
+00:13:59.560 --> 00:14:04.079
+them it can't distinguish what position
+
+00:14:02.320 --> 00:14:05.639
+is east of the Mississippi and what
+
+00:14:04.079 --> 00:14:09.120
+position is the west west of the
+
+00:14:05.639 --> 00:14:11.279
+Mississippi but if you write a uh like a
+
+00:14:09.120 --> 00:14:14.759
+sparkle query it's not that hard to do
+
+00:14:11.279 --> 00:14:16.480
+that so there are um you know there are
+
+00:14:14.759 --> 00:14:18.639
+certain types of questions especially
+
+00:14:16.480 --> 00:14:20.399
+information aggregation and complex
+
+00:14:18.639 --> 00:14:22.839
+relations and stuff that uh language
+
+00:14:20.399 --> 00:14:26.600
+models are not very good
+
+00:14:22.839 --> 00:14:28.120
+at cool um so that's kind of an intro to
+
+00:14:26.600 --> 00:14:31.240
+knowledge bases why you might want to
+
+00:14:28.120 --> 00:14:33.759
+think about them any questions so far
+
+00:14:31.240 --> 00:14:33.759
+for
+
+00:14:34.759 --> 00:14:39.720
+discussion okay um I will move on next
+
+00:14:38.320 --> 00:14:41.199
+so the next thing I'd like to talk about
+
+00:14:39.720 --> 00:14:43.839
+is learning representations for
+
+00:14:41.199 --> 00:14:45.519
+knowledge bases um so knowledge bases
+
+00:14:43.839 --> 00:14:48.000
+are great but one problem is they're
+
+00:14:45.519 --> 00:14:51.040
+like inherently
+
+00:14:48.000 --> 00:14:55.040
+incomplete and even with extremely large
+
+00:14:51.040 --> 00:14:58.279
+scale uh it becomes impossible to have
+
+00:14:55.040 --> 00:15:00.360
+them be complete and the reason why is
+
+00:14:58.279 --> 00:15:03.639
+uh for examp example in Freebase which
+
+00:15:00.360 --> 00:15:05.480
+was the predecessor to Wiki data um 71%
+
+00:15:03.639 --> 00:15:08.560
+of humans didn't have a date of
+
+00:15:05.480 --> 00:15:10.560
+birth um and probably every human
+
+00:15:08.560 --> 00:15:12.079
+actually has a date of birth right um
+
+00:15:10.560 --> 00:15:15.880
+you know we're pretty much guaranteed
+
+00:15:12.079 --> 00:15:17.639
+for that to be the case so the issue is
+
+00:15:15.880 --> 00:15:19.160
+like for very famous entities you want
+
+00:15:17.639 --> 00:15:21.040
+lots of detailed information like you
+
+00:15:19.160 --> 00:15:24.000
+can know absolutely everything about Joe
+
+00:15:21.040 --> 00:15:25.759
+Biden or Barack Obama but you know at
+
+00:15:24.000 --> 00:15:26.880
+the same time for Less major entities
+
+00:15:25.759 --> 00:15:28.079
+you still want them in the knowledge
+
+00:15:26.880 --> 00:15:30.079
+base but you're not going to be able to
+
+00:15:28.079 --> 00:15:31.519
+get all that information or should you
+
+00:15:30.079 --> 00:15:35.600
+for privacy
+
+00:15:31.519 --> 00:15:36.680
+purposes and so the idea is um for
+
+00:15:35.600 --> 00:15:38.079
+information that's written on the
+
+00:15:36.680 --> 00:15:40.600
+internet somewhere can you perform
+
+00:15:38.079 --> 00:15:42.759
+relation extraction which essentially
+
+00:15:40.600 --> 00:15:44.600
+allows you to extract this information
+
+00:15:42.759 --> 00:15:46.360
+and create your own knowledge bases and
+
+00:15:44.600 --> 00:15:47.680
+stuff like this and this can also be
+
+00:15:46.360 --> 00:15:50.079
+useful if you want to create it for like
+
+00:15:47.680 --> 00:15:52.199
+a specialized domain or um or other
+
+00:15:50.079 --> 00:15:55.000
+stuff like
+
+00:15:52.199 --> 00:15:59.519
+that so there's a bunch of ways that
+
+00:15:55.000 --> 00:16:03.079
+people do this um and one kind of
+
+00:15:59.519 --> 00:16:06.120
+popular way that people have tried to do
+
+00:16:03.079 --> 00:16:09.199
+relation extraction is through uh
+
+00:16:06.120 --> 00:16:12.560
+leveraging consistency in embedding
+
+00:16:09.199 --> 00:16:15.319
+space and so this is the most famous
+
+00:16:12.560 --> 00:16:17.959
+example from word de uh what seems like
+
+00:16:15.319 --> 00:16:21.880
+ages ago uh in
+
+00:16:17.959 --> 00:16:23.920
+2013 and in the word Toc paper one of
+
+00:16:21.880 --> 00:16:26.279
+the big you know exciting things was
+
+00:16:23.920 --> 00:16:28.639
+essentially they demonstrated that
+
+00:16:26.279 --> 00:16:30.120
+vectors in embedding space had kind of
+
+00:16:28.639 --> 00:16:31.839
+in
+
+00:16:30.120 --> 00:16:33.160
+you know meaning and actually the
+
+00:16:31.839 --> 00:16:34.600
+vectors in embedding space could
+
+00:16:33.160 --> 00:16:37.639
+correspond to relations between
+
+00:16:34.600 --> 00:16:39.480
+embeddings so like uh we would have man
+
+00:16:37.639 --> 00:16:41.000
+pointing to woman in approximately the
+
+00:16:39.480 --> 00:16:42.920
+same direction that we had Uncle
+
+00:16:41.000 --> 00:16:46.600
+pointing to Aunt and King pointing to
+
+00:16:42.920 --> 00:16:49.680
+Queen and so um then you could do things
+
+00:16:46.600 --> 00:16:51.440
+like you could take Kings subtract out
+
+00:16:49.680 --> 00:16:53.560
+the vector that corresponded to
+
+00:16:51.440 --> 00:16:58.360
+plurality uh add the vector that
+
+00:16:53.560 --> 00:17:00.839
+corresponded to um you know uh to going
+
+00:16:58.360 --> 00:17:04.319
+from masculine to feminine words and
+
+00:17:00.839 --> 00:17:05.559
+then um like read the vector to that
+
+00:17:04.319 --> 00:17:07.160
+were plural and you'd be able to
+
+00:17:05.559 --> 00:17:09.439
+identify the plural by just knowing
+
+00:17:07.160 --> 00:17:11.000
+these two uh vectors the plural of green
+
+00:17:09.439 --> 00:17:14.000
+by just knowing those two
+
+00:17:11.000 --> 00:17:14.000
+vectors
+
+00:17:14.160 --> 00:17:21.880
+um but it turns out that you can either
+
+00:17:18.199 --> 00:17:21.880
+learn embeddings
+
+00:17:22.720 --> 00:17:28.240
+from like uh you can either learn
+
+00:17:25.000 --> 00:17:30.400
+embeddings from text or you can use the
+
+00:17:28.240 --> 00:17:32.039
+fact that you have a big knowledge base
+
+00:17:30.400 --> 00:17:34.880
+that was curated by humans like Wiki
+
+00:17:32.039 --> 00:17:36.120
+data to improve the embeddings of a
+
+00:17:34.880 --> 00:17:39.559
+neural model
+
+00:17:36.120 --> 00:17:41.799
+itself and so another pretty large uh
+
+00:17:39.559 --> 00:17:43.600
+research area that a lot of people have
+
+00:17:41.799 --> 00:17:47.120
+focused on is how do you get good
+
+00:17:43.600 --> 00:17:48.720
+embeddings of a Knowledge Graph and this
+
+00:17:47.120 --> 00:17:50.600
+is important if you want to do any sort
+
+00:17:48.720 --> 00:17:52.799
+of like Knowledge Graph Search or other
+
+00:17:50.600 --> 00:17:54.160
+things like this like for example one of
+
+00:17:52.799 --> 00:17:56.799
+the really nice things about knowledge
+
+00:17:54.160 --> 00:17:58.880
+graphs is they have information about a
+
+00:17:56.799 --> 00:18:00.200
+whole bunch of really sparse entities
+
+00:17:58.880 --> 00:18:03.240
+that aren't mentioned very much on the
+
+00:18:00.200 --> 00:18:05.679
+internet for example and so because of
+
+00:18:03.240 --> 00:18:07.440
+that you can um you can leverage the
+
+00:18:05.679 --> 00:18:10.720
+knowledge graph structure together with
+
+00:18:07.440 --> 00:18:10.720
+text to learn better embeddings
+
+00:18:11.240 --> 00:18:18.520
+overall and so this particular paper is
+
+00:18:15.280 --> 00:18:20.960
+one example of it um and the way they do
+
+00:18:18.520 --> 00:18:23.280
+this is they express uh Knowledge Graph
+
+00:18:20.960 --> 00:18:25.919
+triples is additive
+
+00:18:23.280 --> 00:18:28.480
+Transformations and they minimize the
+
+00:18:25.919 --> 00:18:31.640
+distance uh of existing triples with a
+
+00:18:28.480 --> 00:18:35.039
+margin based loss so the way they do
+
+00:18:31.640 --> 00:18:38.240
+this is they have the head um in the
+
+00:18:35.039 --> 00:18:40.799
+tail and L is the vector corresponding
+
+00:18:38.240 --> 00:18:42.679
+to like the link between the things that
+
+00:18:40.799 --> 00:18:47.960
+corresponds to a
+
+00:18:42.679 --> 00:18:52.159
+relation and so you go uh you have H and
+
+00:18:47.960 --> 00:18:53.559
+T and here um like this is L but here
+
+00:18:52.159 --> 00:18:55.640
+it's written as are because I got this
+
+00:18:53.559 --> 00:18:58.120
+from a different paper and basically you
+
+00:18:55.640 --> 00:18:59.480
+you try to go from H to T um according
+
+00:18:58.120 --> 00:19:00.919
+to the relation
+
+00:18:59.480 --> 00:19:05.120
+uh Vector
+
+00:19:00.919 --> 00:19:07.200
+are and you use a hinge loss where um
+
+00:19:05.120 --> 00:19:10.039
+for the hinge loss you you have a hinge
+
+00:19:07.200 --> 00:19:12.640
+parameter and then you try to upweight
+
+00:19:10.039 --> 00:19:15.760
+the example of a true triple and
+
+00:19:12.640 --> 00:19:17.960
+downweight the example of a of a false
+
+00:19:15.760 --> 00:19:19.880
+triple so this could be one that was
+
+00:19:17.960 --> 00:19:22.080
+like randomly sampled to be incorrect
+
+00:19:19.880 --> 00:19:22.080
+for
+
+00:19:23.760 --> 00:19:29.080
+example um one interesting thing about
+
+00:19:26.880 --> 00:19:31.559
+knowledge graph embeddings is like a lot
+
+00:19:29.080 --> 00:19:33.600
+of famous AI researchers got their start
+
+00:19:31.559 --> 00:19:36.000
+in Knowledge Graph embeddings and so
+
+00:19:33.600 --> 00:19:39.760
+Richard soer is one of them if you know
+
+00:19:36.000 --> 00:19:44.320
+he's the CEO of vi.com search engine now
+
+00:19:39.760 --> 00:19:46.679
+um and uh this was a first attempt at
+
+00:19:44.320 --> 00:19:49.679
+predicting relations they basically
+
+00:19:46.679 --> 00:19:55.400
+created a um MLP that tries to predict
+
+00:19:49.679 --> 00:19:58.880
+whether a relation exists so they have
+
+00:19:55.400 --> 00:20:00.760
+a matrix for the left side of the
+
+00:19:58.880 --> 00:20:03.320
+relation a matrix for the right side of
+
+00:20:00.760 --> 00:20:05.080
+the relation and then they feed in the
+
+00:20:03.320 --> 00:20:07.559
+embeddings of each of the entities in
+
+00:20:05.080 --> 00:20:08.919
+the relation they have a nonlinearity
+
+00:20:07.559 --> 00:20:11.799
+and then they have another Vector that
+
+00:20:08.919 --> 00:20:14.720
+tries to predict the um the probability
+
+00:20:11.799 --> 00:20:16.679
+of the uh actual relation being correct
+
+00:20:14.720 --> 00:20:18.960
+so you would run this through a sigmoid
+
+00:20:16.679 --> 00:20:21.000
+and then uh if it was one the relation
+
+00:20:18.960 --> 00:20:24.039
+was likely to exist if it was Zero then
+
+00:20:21.000 --> 00:20:25.480
+the relation was likely to not exist and
+
+00:20:24.039 --> 00:20:27.799
+then they also propos something called a
+
+00:20:25.480 --> 00:20:31.480
+neural tensor Network and this adds a
+
+00:20:27.799 --> 00:20:34.000
+bilinear feature extractor um and so
+
+00:20:31.480 --> 00:20:37.440
+basically what this is saying is we have
+
+00:20:34.000 --> 00:20:40.000
+the embedding here the embedding here we
+
+00:20:37.440 --> 00:20:41.840
+have a matrix and then we calculate the
+
+00:20:40.000 --> 00:20:43.080
+dot product between the embedding after
+
+00:20:41.840 --> 00:20:45.799
+transformation it looks a lot like
+
+00:20:43.080 --> 00:20:47.720
+attention actually in a way um because
+
+00:20:45.799 --> 00:20:50.000
+we had the bilinear attention so it's
+
+00:20:47.720 --> 00:20:53.640
+similar to that as well and then we also
+
+00:20:50.000 --> 00:20:56.840
+have the MLP so this part corresponds to
+
+00:20:53.640 --> 00:21:00.320
+MLP and then we have a bias
+
+00:20:56.840 --> 00:21:02.200
+term and um this is a powerful model but
+
+00:21:00.320 --> 00:21:05.400
+it's a bit overparameterized so we
+
+00:21:02.200 --> 00:21:08.120
+actually later um uh this kind of fell
+
+00:21:05.400 --> 00:21:10.360
+out of uh favor towards these more
+
+00:21:08.120 --> 00:21:14.520
+simple models that we're using uh kind
+
+00:21:10.360 --> 00:21:14.520
+of just linear projections between the
+
+00:21:17.600 --> 00:21:22.279
+two so there's um there's a lot of
+
+00:21:20.120 --> 00:21:25.320
+methods like this these methods are
+
+00:21:22.279 --> 00:21:27.039
+basically assuming that we have either
+
+00:21:25.320 --> 00:21:29.080
+Knowledge Graph
+
+00:21:27.039 --> 00:21:30.799
+embeddings um and we want to learn
+
+00:21:29.080 --> 00:21:32.480
+relations or they're assuming that we
+
+00:21:30.799 --> 00:21:34.320
+don't have any information at all about
+
+00:21:32.480 --> 00:21:36.840
+the knowledge graph and we want to learn
+
+00:21:34.320 --> 00:21:40.039
+the knowledge graph embedding themselves
+
+00:21:36.840 --> 00:21:42.400
+it's been used for both of them but um I
+
+00:21:40.039 --> 00:21:44.000
+I'd say now it's probably most useful
+
+00:21:42.400 --> 00:21:45.520
+for learning Knowledge Graph embeddings
+
+00:21:44.000 --> 00:21:50.480
+if you want to do any sort of Knowledge
+
+00:21:45.520 --> 00:21:50.480
+Graph based modeling uh which can be
+
+00:21:51.240 --> 00:21:55.919
+useful um cool any questions about these
+
+00:21:57.360 --> 00:22:01.679
+ones okay
+
+00:21:59.520 --> 00:22:04.360
+next um actually this part might be a
+
+00:22:01.679 --> 00:22:06.600
+little bit simpler than the uh than the
+
+00:22:04.360 --> 00:22:09.000
+like knowledge graft based approaches so
+
+00:22:06.600 --> 00:22:10.960
+another method for relations extraction
+
+00:22:09.000 --> 00:22:13.440
+is learning from text
+
+00:22:10.960 --> 00:22:16.120
+directly
+
+00:22:13.440 --> 00:22:19.080
+and the first question about this is how
+
+00:22:16.120 --> 00:22:22.200
+do you get training data to learn uh
+
+00:22:19.080 --> 00:22:24.480
+about relation learn relation extraction
+
+00:22:22.200 --> 00:22:26.720
+and so there was this very influential
+
+00:22:24.480 --> 00:22:28.279
+paper a distant supervision for relation
+
+00:22:26.720 --> 00:22:31.120
+extraction I would say it's almost one
+
+00:22:28.279 --> 00:22:32.880
+of the first or certainly one of the
+
+00:22:31.120 --> 00:22:34.559
+most influential papers on like data
+
+00:22:32.880 --> 00:22:35.960
+augmentation or synthetic data for
+
+00:22:34.559 --> 00:22:38.400
+natural language
+
+00:22:35.960 --> 00:22:40.440
+processing and basically the idea is you
+
+00:22:38.400 --> 00:22:44.279
+already have a knowledge base that has
+
+00:22:40.440 --> 00:22:47.440
+some entries in it like Wiki data and so
+
+00:22:44.279 --> 00:22:50.919
+then given in entity relation entity
+
+00:22:47.440 --> 00:22:52.919
+triples um can you extract all text that
+
+00:22:50.919 --> 00:22:54.799
+matches this particular relation type
+
+00:22:52.919 --> 00:22:56.480
+and use it to train a relation extractor
+
+00:22:54.799 --> 00:22:59.640
+a supervised relation
+
+00:22:56.480 --> 00:23:01.880
+extractor so the way this works
+
+00:22:59.640 --> 00:23:04.039
+is like let's say we have this is an old
+
+00:23:01.880 --> 00:23:06.120
+paper so the examples are also old but
+
+00:23:04.039 --> 00:23:08.039
+um let's say we have Steven Spielberg
+
+00:23:06.120 --> 00:23:10.159
+being a director of the film Saving
+
+00:23:08.039 --> 00:23:12.840
+Private Ryan and that's included in our
+
+00:23:10.159 --> 00:23:14.840
+uh our knowledge base so what it would
+
+00:23:12.840 --> 00:23:17.080
+do is it would find all sentences that
+
+00:23:14.840 --> 00:23:19.400
+have Steven Spielberg and Saving Private
+
+00:23:17.080 --> 00:23:22.080
+Ryan included in them and it would label
+
+00:23:19.400 --> 00:23:24.159
+this as like a positive example of that
+
+00:23:22.080 --> 00:23:28.240
+relation so this
+
+00:23:24.159 --> 00:23:30.760
+is in general often it's okay it it
+
+00:23:28.240 --> 00:23:34.480
+works reasonably well but the problem
+
+00:23:30.760 --> 00:23:37.200
+with this is there are also um negative
+
+00:23:34.480 --> 00:23:38.840
+examples of this so like for example
+
+00:23:37.200 --> 00:23:40.480
+here I think the first one is kind of a
+
+00:23:38.840 --> 00:23:43.240
+negative example for the director
+
+00:23:40.480 --> 00:23:45.880
+relation because Steven Spielberg's film
+
+00:23:43.240 --> 00:23:48.120
+Saving Private Ryan doesn't actually
+
+00:23:45.880 --> 00:23:50.000
+tell you he's the director it just tells
+
+00:23:48.120 --> 00:23:52.520
+you that he's somehow affiliated with it
+
+00:23:50.000 --> 00:23:54.840
+he could be the writer or he could be uh
+
+00:23:52.520 --> 00:23:57.679
+the actor or or something else like that
+
+00:23:54.840 --> 00:24:00.440
+so this is a nice way to create data for
+
+00:23:57.679 --> 00:24:03.640
+basically free but at the same time uh
+
+00:24:00.440 --> 00:24:06.159
+you can like create noisy examples and
+
+00:24:03.640 --> 00:24:06.159
+that can be a
+
+00:24:07.159 --> 00:24:14.600
+problem so um there's been a lot of work
+
+00:24:11.400 --> 00:24:16.000
+about this um relationship uh relation
+
+00:24:14.600 --> 00:24:17.840
+classification with neural networks
+
+00:24:16.000 --> 00:24:20.840
+there's a lot of uh different methods
+
+00:24:17.840 --> 00:24:23.159
+that could be uh doing this most of them
+
+00:24:20.840 --> 00:24:24.919
+work by extracting features and then
+
+00:24:23.159 --> 00:24:27.039
+classifying somehow although there are
+
+00:24:24.919 --> 00:24:29.960
+some uh large language model based
+
+00:24:27.039 --> 00:24:33.120
+methods now um one one thing about
+
+00:24:29.960 --> 00:24:35.440
+relation extraction or not kind of like
+
+00:24:33.120 --> 00:24:36.799
+information extraction in general is
+
+00:24:35.440 --> 00:24:38.559
+that very often you want to run this
+
+00:24:36.799 --> 00:24:40.200
+over like a huge Corpus you want to run
+
+00:24:38.559 --> 00:24:42.320
+it over the whole internet or other
+
+00:24:40.200 --> 00:24:45.000
+things like that so from that point of
+
+00:24:42.320 --> 00:24:47.159
+view like I I said I could just ask
+
+00:24:45.000 --> 00:24:49.480
+mistol to give me the answer about like
+
+00:24:47.159 --> 00:24:52.440
+whether cars are included in sentences
+
+00:24:49.480 --> 00:24:55.120
+but if you want to run you know gp4 over
+
+00:24:52.440 --> 00:24:56.799
+the whole internet that's a pretty big
+
+00:24:55.120 --> 00:25:00.159
+budget and you might want to reconsider
+
+00:24:56.799 --> 00:25:02.440
+that so there are so um there is also
+
+00:25:00.159 --> 00:25:04.440
+some you know benefit in having cheap
+
+00:25:02.440 --> 00:25:07.200
+and lightweight
+
+00:25:04.440 --> 00:25:09.159
+methods so basically what this
+
+00:25:07.200 --> 00:25:11.279
+particular paper did is it extracted
+
+00:25:09.159 --> 00:25:12.760
+features in in classified so it
+
+00:25:11.279 --> 00:25:15.600
+extracted lexical features of the
+
+00:25:12.760 --> 00:25:20.240
+entities themselves and features of the
+
+00:25:15.600 --> 00:25:22.360
+whole span and so like the way I uh most
+
+00:25:20.240 --> 00:25:26.960
+modern methods for this do this is they
+
+00:25:22.360 --> 00:25:29.399
+basically um extract features from the
+
+00:25:26.960 --> 00:25:31.679
+first part of the first entity the
+
+00:25:29.399 --> 00:25:33.760
+second part of the the first entity the
+
+00:25:31.679 --> 00:25:36.360
+first part of the second entity and the
+
+00:25:33.760 --> 00:25:37.720
+last part of the uh second entity and
+
+00:25:36.360 --> 00:25:39.600
+take all of those embeddings feed them
+
+00:25:37.720 --> 00:25:41.440
+into like an MLP or something like that
+
+00:25:39.600 --> 00:25:44.039
+and then make a prediction about whether
+
+00:25:41.440 --> 00:25:45.760
+that relation exists so if you have an
+
+00:25:44.039 --> 00:25:47.840
+embedding model this is relatively easy
+
+00:25:45.760 --> 00:25:50.360
+to do you feed it through like uh
+
+00:25:47.840 --> 00:25:51.919
+Roberta or you feed it through mistol
+
+00:25:50.360 --> 00:25:54.559
+and get the embeddings for each of the
+
+00:25:51.919 --> 00:25:55.840
+tokens and um and then you make a
+
+00:25:54.559 --> 00:25:58.840
+prediction based on those four
+
+00:25:55.840 --> 00:25:58.840
+embeddings
+
+00:26:00.600 --> 00:26:04.840
+um the details of that are like not
+
+00:26:03.520 --> 00:26:07.320
+super important unless you're going to
+
+00:26:04.840 --> 00:26:09.279
+go in and implement it yourself so you
+
+00:26:07.320 --> 00:26:10.919
+can um like if you're actually going to
+
+00:26:09.279 --> 00:26:12.120
+be doing relation extraction obviously
+
+00:26:10.919 --> 00:26:14.279
+the details are important but I'm
+
+00:26:12.120 --> 00:26:16.000
+assuming that most people won't be uh
+
+00:26:14.279 --> 00:26:19.720
+you know doing that as your final
+
+00:26:16.000 --> 00:26:21.240
+project but um one really interesting
+
+00:26:19.720 --> 00:26:22.919
+thing that is relevant even if you're
+
+00:26:21.240 --> 00:26:26.360
+not doing relationship relation
+
+00:26:22.919 --> 00:26:29.360
+extraction is how you can model noise
+
+00:26:26.360 --> 00:26:32.600
+because this um as I said they're
+
+00:26:29.360 --> 00:26:35.720
+creating lots of like semi noisy data
+
+00:26:32.600 --> 00:26:38.919
+and a lot of the work in getting good
+
+00:26:35.720 --> 00:26:40.360
+bottles for relation extraction has been
+
+00:26:38.919 --> 00:26:41.799
+how do we deal with this distant
+
+00:26:40.360 --> 00:26:43.799
+supervision noise and I'm just going to
+
+00:26:41.799 --> 00:26:45.760
+give one example here but there's like a
+
+00:26:43.799 --> 00:26:49.120
+series of papers after this that also
+
+00:26:45.760 --> 00:26:50.600
+tried to do similar things so the idea
+
+00:26:49.120 --> 00:26:53.600
+is that there's noise in the distant
+
+00:26:50.600 --> 00:26:56.559
+supervision labels um and so we want to
+
+00:26:53.600 --> 00:27:01.039
+model and mitigate that noise and the
+
+00:26:56.559 --> 00:27:03.919
+way this paper does this is they have an
+
+00:27:01.039 --> 00:27:06.679
+encoder and from the encoder you
+
+00:27:03.919 --> 00:27:10.960
+calculate embeddings and make
+
+00:27:06.679 --> 00:27:14.279
+predictions and so you have a small set
+
+00:27:10.960 --> 00:27:16.080
+of like very high quality data and this
+
+00:27:14.279 --> 00:27:17.760
+small set of very high quality data you
+
+00:27:16.080 --> 00:27:19.880
+can basically trust that all of the data
+
+00:27:17.760 --> 00:27:22.320
+is not noisy like maybe it's manually
+
+00:27:19.880 --> 00:27:23.720
+annotated data and you have like 5,000
+
+00:27:22.320 --> 00:27:25.000
+examples of it or something like that
+
+00:27:23.720 --> 00:27:26.880
+and then separately from that you have
+
+00:27:25.000 --> 00:27:28.440
+like 5 million examples of automatically
+
+00:27:26.880 --> 00:27:30.799
+labeled data that might be good might
+
+00:27:28.440 --> 00:27:32.679
+not be good and so what they do is
+
+00:27:30.799 --> 00:27:34.200
+essentially at the beginning they take
+
+00:27:32.679 --> 00:27:36.520
+this encoder get embeddings make
+
+00:27:34.200 --> 00:27:38.000
+predictions over the high quality data
+
+00:27:36.520 --> 00:27:40.320
+and then they have a separate noise
+
+00:27:38.000 --> 00:27:43.440
+modeling layer where what this noise
+
+00:27:40.320 --> 00:27:46.919
+modeling layer does is it has a
+
+00:27:43.440 --> 00:27:50.039
+transition Matrix which says given that
+
+00:27:46.919 --> 00:27:53.279
+this given that we made a particular
+
+00:27:50.039 --> 00:27:55.159
+prediction over classes because this is
+
+00:27:53.279 --> 00:27:59.919
+essentially a multiclass classification
+
+00:27:55.159 --> 00:28:01.519
+problem they transform the
+
+00:27:59.919 --> 00:28:03.159
+sorry I don't remember if they transform
+
+00:28:01.519 --> 00:28:04.640
+the probabilities or the low Jets I
+
+00:28:03.159 --> 00:28:07.320
+think it's the probabilities but they
+
+00:28:04.640 --> 00:28:12.799
+transform the probabilities and get a
+
+00:28:07.320 --> 00:28:14.720
+final uh distribution after noise and so
+
+00:28:12.799 --> 00:28:17.399
+that means that you can basically smooth
+
+00:28:14.720 --> 00:28:19.240
+out this uh distribution and account for
+
+00:28:17.399 --> 00:28:20.880
+the fact that the labels may be noisy or
+
+00:28:19.240 --> 00:28:24.399
+may may not be
+
+00:28:20.880 --> 00:28:26.600
+noisy um then they add additional
+
+00:28:24.399 --> 00:28:28.559
+normalization on this transition Matrix
+
+00:28:26.600 --> 00:28:32.440
+using something called Trace normal
+
+00:28:28.559 --> 00:28:35.840
+ization to move this Matrix closer to
+
+00:28:32.440 --> 00:28:38.480
+the identity function which says that
+
+00:28:35.840 --> 00:28:40.720
+the predictions are probably not wrong
+
+00:28:38.480 --> 00:28:43.159
+all the time uh the predictions are
+
+00:28:40.720 --> 00:28:45.360
+probably correct you know a lot of the
+
+00:28:43.159 --> 00:28:46.600
+time they're not correct all the time uh
+
+00:28:45.360 --> 00:28:49.720
+so then you have that Trace
+
+00:28:46.600 --> 00:28:51.880
+normalization competing with um this uh
+
+00:28:49.720 --> 00:28:55.440
+trying to give you like a more smooth
+
+00:28:51.880 --> 00:28:58.760
+distribution and and reduce your uh L
+
+00:28:55.440 --> 00:29:00.320
+like reduce your loss so um I I think
+
+00:28:58.760 --> 00:29:02.559
+this is actually a pretty interesting
+
+00:29:00.320 --> 00:29:04.480
+idea and it can be used not just for
+
+00:29:02.559 --> 00:29:08.600
+relation extraction but also in cases
+
+00:29:04.480 --> 00:29:08.600
+where um you might have noisy labels
+
+00:29:08.799 --> 00:29:14.320
+overall um so are there any questions
+
+00:29:12.360 --> 00:29:15.720
+about this or any of the things that are
+
+00:29:14.320 --> 00:29:18.480
+going on
+
+00:29:15.720 --> 00:29:20.279
+here um even if you're completely
+
+00:29:18.480 --> 00:29:21.960
+uninterested in relation extraction I'd
+
+00:29:20.279 --> 00:29:23.720
+encourage you to think about like what
+
+00:29:21.960 --> 00:29:26.159
+are
+
+00:29:23.720 --> 00:29:27.360
+some examples of things that you are
+
+00:29:26.159 --> 00:29:29.519
+interested in where you could get
+
+00:29:27.360 --> 00:29:31.840
+potentially labels and how could you for
+
+00:29:29.519 --> 00:29:34.880
+theise there like that might be uh you
+
+00:29:31.840 --> 00:29:34.880
+know a thing to
+
+00:29:35.679 --> 00:29:39.919
+about okay so this was a very very brief
+
+00:29:38.320 --> 00:29:42.679
+overview of how we create knowledge
+
+00:29:39.919 --> 00:29:44.080
+bases uh from textual data or from
+
+00:29:42.679 --> 00:29:47.159
+Knowledge Graph data structured
+
+00:29:44.080 --> 00:29:48.840
+Knowledge Graph data um so now I like to
+
+00:29:47.159 --> 00:29:51.519
+talk a little bit about how to use
+
+00:29:48.840 --> 00:29:53.960
+knowledge bases to inform neural
+
+00:29:51.519 --> 00:29:56.159
+models and there's a bunch of different
+
+00:29:53.960 --> 00:29:59.519
+ways to do this
+
+00:29:56.159 --> 00:30:02.600
+um the
+
+00:29:59.519 --> 00:30:06.960
+the first way um is to
+
+00:30:02.600 --> 00:30:09.840
+improve embeddings uh
+
+00:30:06.960 --> 00:30:11.960
+with existing lexicons and this example
+
+00:30:09.840 --> 00:30:14.679
+is using non-contextual embeddings like
+
+00:30:11.960 --> 00:30:16.240
+not the not the ones we get from neural
+
+00:30:14.679 --> 00:30:17.919
+language models but once we get from
+
+00:30:16.240 --> 00:30:20.919
+just running a embedding model like word
+
+00:30:17.919 --> 00:30:22.960
+toac or something like this um and what
+
+00:30:20.919 --> 00:30:25.640
+they did in this paper is they
+
+00:30:22.960 --> 00:30:27.600
+essentially um retrofitted embeddings to
+
+00:30:25.640 --> 00:30:30.840
+existing lexicons by doing post Hawk
+
+00:30:27.600 --> 00:30:34.080
+trans of the embeddings so that they
+
+00:30:30.840 --> 00:30:36.840
+matched the um the knowledge graph for
+
+00:30:34.080 --> 00:30:39.080
+lexon better and so the way they did
+
+00:30:36.840 --> 00:30:41.880
+this is
+
+00:30:39.080 --> 00:30:43.720
+um they started out with pre-trained
+
+00:30:41.880 --> 00:30:45.399
+embeddings and they had a double
+
+00:30:43.720 --> 00:30:47.240
+objective of making the transform
+
+00:30:45.399 --> 00:30:49.120
+embeddings close to the neighbors and
+
+00:30:47.240 --> 00:30:52.519
+close to the original
+
+00:30:49.120 --> 00:30:58.840
+embedding and the way they did this is
+
+00:30:52.519 --> 00:30:58.840
+they essentially had um this
+
+00:30:59.799 --> 00:31:03.720
+this regularization term over here so
+
+00:31:01.880 --> 00:31:06.200
+this regularization term is basically
+
+00:31:03.720 --> 00:31:08.279
+saying um I don't want you to move your
+
+00:31:06.200 --> 00:31:09.360
+embeddings too far away from how they
+
+00:31:08.279 --> 00:31:11.679
+were
+
+00:31:09.360 --> 00:31:14.799
+initialized and then at the same time I
+
+00:31:11.679 --> 00:31:17.279
+would like you to make these uh
+
+00:31:14.799 --> 00:31:19.600
+embeddings closer to each other if they
+
+00:31:17.279 --> 00:31:21.240
+are synonyms of each other so they did
+
+00:31:19.600 --> 00:31:23.600
+this using word net and they basically
+
+00:31:21.240 --> 00:31:26.200
+took the words uh that were synonyms to
+
+00:31:23.600 --> 00:31:28.679
+each other in sinets with each other and
+
+00:31:26.200 --> 00:31:30.000
+they tried to regularize the synonyms to
+
+00:31:28.679 --> 00:31:32.120
+be closer together but also the
+
+00:31:30.000 --> 00:31:33.639
+embeddings to be closer to how they
+
+00:31:32.120 --> 00:31:35.960
+started
+
+00:31:33.639 --> 00:31:38.799
+out and there were also examples of
+
+00:31:35.960 --> 00:31:40.720
+forcing anms away from each other so
+
+00:31:38.799 --> 00:31:42.480
+like if you're um this is a little bit
+
+00:31:40.720 --> 00:31:44.799
+of an older work so it was working on
+
+00:31:42.480 --> 00:31:47.600
+non-contextualized embeddings but we
+
+00:31:44.799 --> 00:31:49.399
+could do something very similar for um
+
+00:31:47.600 --> 00:31:52.000
+more modern models in like Knowledge
+
+00:31:49.399 --> 00:31:55.320
+Graph embeddings for example so let's
+
+00:31:52.000 --> 00:31:58.960
+say we had
+
+00:31:55.320 --> 00:32:03.240
+um a model that ident
+
+00:31:58.960 --> 00:32:06.600
+entities and then different examples of
+
+00:32:03.240 --> 00:32:06.600
+those entities across different
+
+00:32:07.159 --> 00:32:11.480
+contexts um let's go back to the wiki
+
+00:32:20.639 --> 00:32:26.840
+data and so um if we had lots of
+
+00:32:23.960 --> 00:32:29.360
+examples of Joe Biden um Joe Biden is
+
+00:32:26.840 --> 00:32:35.159
+referred to in a number ways like Joe
+
+00:32:29.360 --> 00:32:44.440
+Biden Joseph Biden Joseph R Biden um J
+
+00:32:35.159 --> 00:32:47.880
+jrb I guess um pus 48 46 sorry um and uh
+
+00:32:44.440 --> 00:32:50.799
+so you could find different examples of
+
+00:32:47.880 --> 00:32:52.799
+things that match these strings um and
+
+00:32:50.799 --> 00:32:55.360
+even do entity linking uh which I'll
+
+00:32:52.799 --> 00:32:57.200
+I'll talk about in a little bit and then
+
+00:32:55.360 --> 00:32:58.760
+encourag the embeddings for all of these
+
+00:32:57.200 --> 00:33:01.360
+different instances is to be closer
+
+00:32:58.760 --> 00:33:04.039
+together to make your model like disting
+
+00:33:01.360 --> 00:33:06.799
+uh distinguish them less and Ure that
+
+00:33:04.039 --> 00:33:08.399
+they uh they get closer edings and that
+
+00:33:06.799 --> 00:33:11.639
+could improve like question answering
+
+00:33:08.399 --> 00:33:11.639
+look up other stuff like
+
+00:33:12.960 --> 00:33:19.880
+that
+
+00:33:14.919 --> 00:33:23.399
+cool um yeah I have a question about
+
+00:33:19.880 --> 00:33:25.399
+this so what happens if you do like subw
+
+00:33:23.399 --> 00:33:28.000
+modeling and then you don't have like
+
+00:33:25.399 --> 00:33:30.440
+the embedment for that entire string
+
+00:33:28.000 --> 00:33:32.320
+that is supposed to be Clos yeah what
+
+00:33:30.440 --> 00:33:34.279
+happens if you do subword modeling and
+
+00:33:32.320 --> 00:33:35.480
+you don't have the embedding uh you
+
+00:33:34.279 --> 00:33:37.159
+don't have a single embedding that
+
+00:33:35.480 --> 00:33:40.360
+corresponds to an entity so that's a
+
+00:33:37.159 --> 00:33:42.559
+really good question um let me
+
+00:33:40.360 --> 00:33:44.240
+check I don't think I actually have
+
+00:33:42.559 --> 00:33:46.600
+these on the slide so I might have to
+
+00:33:44.240 --> 00:33:46.600
+open a
+
+00:33:53.639 --> 00:33:59.720
+paper yeah okay so there's a lot of
+
+00:33:56.440 --> 00:33:59.720
+different ways to handle this
+
+00:34:11.520 --> 00:34:18.079
+so there there's two papers um the first
+
+00:34:14.879 --> 00:34:20.000
+paper is uh a really nice paper very
+
+00:34:18.079 --> 00:34:22.359
+influential on the subject of
+
+00:34:20.000 --> 00:34:25.359
+co-reference resolution and co-reference
+
+00:34:22.359 --> 00:34:27.240
+resolution um is essentially trying to
+
+00:34:25.359 --> 00:34:30.000
+identify when two spans correspond to
+
+00:34:27.240 --> 00:34:32.320
+each other so like if I say Joe B Joe
+
+00:34:30.000 --> 00:34:34.359
+Biden early in a document and then later
+
+00:34:32.320 --> 00:34:35.480
+in a document it just says Biden we want
+
+00:34:34.359 --> 00:34:38.839
+to know that those two things are
+
+00:34:35.480 --> 00:34:40.919
+referring to each other and then um we
+
+00:34:38.839 --> 00:34:42.839
+had a paper later where we generalized
+
+00:34:40.919 --> 00:34:44.839
+this and applied you know very similar
+
+00:34:42.839 --> 00:34:48.079
+methodology to like lots and lots of
+
+00:34:44.839 --> 00:34:50.760
+different analysis tasks but I can um I
+
+00:34:48.079 --> 00:34:53.839
+can show the beginning here and
+
+00:34:50.760 --> 00:34:59.320
+basically the methodology that they use
+
+00:34:53.839 --> 00:35:02.440
+here um is they add
+
+00:34:59.320 --> 00:35:04.440
+a and this is specifically for modeling
+
+00:35:02.440 --> 00:35:08.240
+spans and getting embeddings out of
+
+00:35:04.440 --> 00:35:09.040
+spans of uh tokens and what they did is
+
+00:35:08.240 --> 00:35:13.079
+they
+
+00:35:09.040 --> 00:35:14.920
+essentially have a model where you take
+
+00:35:13.079 --> 00:35:16.440
+the thing from the beginning the
+
+00:35:14.920 --> 00:35:18.760
+embedding from the beginning of the span
+
+00:35:16.440 --> 00:35:22.040
+the embedding from the end of the span
+
+00:35:18.760 --> 00:35:24.280
+and the average embedding of all of the
+
+00:35:22.040 --> 00:35:26.280
+embeddings in the span and that gives
+
+00:35:24.280 --> 00:35:27.480
+you three vectors for any span right
+
+00:35:26.280 --> 00:35:30.160
+because you can always get the beginning
+
+00:35:27.480 --> 00:35:33.280
+that and in the mean and then based on
+
+00:35:30.160 --> 00:35:36.560
+that they feed that through um like a
+
+00:35:33.280 --> 00:35:37.800
+neural network and get a new edting so
+
+00:35:36.560 --> 00:35:40.000
+they feed that through a transformation
+
+00:35:37.800 --> 00:35:42.520
+and get a new edting and so that's the
+
+00:35:40.000 --> 00:35:44.200
+method that they used and I think our
+
+00:35:42.520 --> 00:35:46.640
+paper actually has a
+
+00:35:44.200 --> 00:35:49.640
+better
+
+00:35:46.640 --> 00:35:52.640
+um a better figure of how you can
+
+00:35:49.640 --> 00:35:56.680
+actually use that actually maybe it
+
+00:35:52.640 --> 00:35:58.160
+doesn't okay but anyway um yeah because
+
+00:35:56.680 --> 00:36:00.240
+uh yeah here's the figure
+
+00:35:58.160 --> 00:36:01.520
+so then you can use that for a number of
+
+00:36:00.240 --> 00:36:03.040
+things you could use that to like look
+
+00:36:01.520 --> 00:36:06.359
+up something in a knowledge base you
+
+00:36:03.040 --> 00:36:08.599
+could also use that to um decide whether
+
+00:36:06.359 --> 00:36:10.440
+two spans are co-referent by feeding in
+
+00:36:08.599 --> 00:36:12.800
+like the first span and the second Span
+
+00:36:10.440 --> 00:36:14.960
+in and then predicting whether those two
+
+00:36:12.800 --> 00:36:19.640
+spans cor correspond to each other or
+
+00:36:14.960 --> 00:36:21.240
+not so this general idea of modeling
+
+00:36:19.640 --> 00:36:22.960
+spans and then modeling relations
+
+00:36:21.240 --> 00:36:24.520
+between the spans allows you to solve
+
+00:36:22.960 --> 00:36:26.119
+like lots of different tasks like part
+
+00:36:24.520 --> 00:36:27.920
+of speech tagging or named entity
+
+00:36:26.119 --> 00:36:30.319
+recognition or relation extraction or
+
+00:36:27.920 --> 00:36:31.920
+other stuff like that so um yeah
+
+00:36:30.319 --> 00:36:34.040
+actually I realized now that I should
+
+00:36:31.920 --> 00:36:35.079
+have probably talked about these in the
+
+00:36:34.040 --> 00:36:36.560
+slides where I was talking about
+
+00:36:35.079 --> 00:36:38.599
+modeling but that that would be my
+
+00:36:36.560 --> 00:36:42.319
+recommended way of doing
+
+00:36:38.599 --> 00:36:42.319
+it cool any other
+
+00:36:43.839 --> 00:36:49.480
+questions nice okay
+
+00:36:46.880 --> 00:36:52.880
+um
+
+00:36:49.480 --> 00:36:55.119
+so another question is how can we inject
+
+00:36:52.880 --> 00:36:56.640
+knowledge into language models um
+
+00:36:55.119 --> 00:36:58.720
+there's a bunch of different ways to do
+
+00:36:56.640 --> 00:37:03.079
+this um
+
+00:36:58.720 --> 00:37:05.000
+one very easy way is to somehow look up
+
+00:37:03.079 --> 00:37:09.640
+relevant knowledge in your knowledge
+
+00:37:05.000 --> 00:37:09.640
+graph and um oh
+
+00:37:10.280 --> 00:37:15.440
+sorry I was presenting on my own screen
+
+00:37:13.040 --> 00:37:18.240
+not the screen that everybody can see so
+
+00:37:15.440 --> 00:37:22.000
+um to look up all of the uh knowledge in
+
+00:37:18.240 --> 00:37:24.000
+a Knowledge Graph and um somehow provide
+
+00:37:22.000 --> 00:37:26.800
+it to the model one way you can provide
+
+00:37:24.000 --> 00:37:28.720
+it to the model is through prompting um
+
+00:37:26.800 --> 00:37:32.400
+but the problem with with prompting is
+
+00:37:28.720 --> 00:37:33.920
+that you're not necessarily going to uh
+
+00:37:32.400 --> 00:37:37.319
+be able
+
+00:37:33.920 --> 00:37:41.359
+to utilize knowledge that is kind of
+
+00:37:37.319 --> 00:37:43.920
+like minority knowledge because the
+
+00:37:41.359 --> 00:37:47.560
+embeddings of the entities that you're
+
+00:37:43.920 --> 00:37:49.440
+presenting may not be you know like well
+
+00:37:47.560 --> 00:37:51.839
+learned so
+
+00:37:49.440 --> 00:37:53.200
+you're requiring essentially the model
+
+00:37:51.839 --> 00:37:55.359
+to be able to generalize from the
+
+00:37:53.200 --> 00:37:57.880
+knowledge you provide in
+
+00:37:55.359 --> 00:38:00.839
+the prompt despite the fact that the
+
+00:37:57.880 --> 00:38:02.240
+prompt is like minor entities or other
+
+00:38:00.839 --> 00:38:07.040
+things like that that are not as well
+
+00:38:02.240 --> 00:38:10.400
+learned so is another um method to
+
+00:38:07.040 --> 00:38:13.440
+handle this um we previously proposed a
+
+00:38:10.400 --> 00:38:15.599
+method that allows you
+
+00:38:13.440 --> 00:38:18.319
+to essentially
+
+00:38:15.599 --> 00:38:21.319
+predict instead of predicting directly
+
+00:38:18.319 --> 00:38:24.920
+the words here you can predict a tag
+
+00:38:21.319 --> 00:38:27.200
+that says birth name or a given name or
+
+00:38:24.920 --> 00:38:31.480
+family name or something like that and
+
+00:38:27.200 --> 00:38:32.839
+then post talk the model will fill in uh
+
+00:38:31.480 --> 00:38:36.720
+that like birth
+
+00:38:32.839 --> 00:38:39.400
+name text based on a knowledge base so
+
+00:38:36.720 --> 00:38:41.079
+um you know if you have a a Wikipedia
+
+00:38:39.400 --> 00:38:44.240
+article about Barack Obama that you're
+
+00:38:41.079 --> 00:38:48.680
+trying to write it could predict um
+
+00:38:44.240 --> 00:38:52.040
+birth name born uh birth name comma born
+
+00:38:48.680 --> 00:38:55.359
+in birth date and that's like a very
+
+00:38:52.040 --> 00:38:56.880
+very common thing in Wikipedia right so
+
+00:38:55.359 --> 00:39:00.960
+because of that it can predict it very
+
+00:38:56.880 --> 00:39:03.160
+consistently very uh formulaically and
+
+00:39:00.960 --> 00:39:04.599
+that allows you to um you know with high
+
+00:39:03.160 --> 00:39:06.079
+confidence get something that makes
+
+00:39:04.599 --> 00:39:08.599
+sense and is factual and reduce
+
+00:39:06.079 --> 00:39:11.400
+hallucination and other stuff like that
+
+00:39:08.599 --> 00:39:12.599
+so um basically how could you inject
+
+00:39:11.400 --> 00:39:14.280
+this into language models there's
+
+00:39:12.599 --> 00:39:16.240
+multiple ways one is prompting that's
+
+00:39:14.280 --> 00:39:18.160
+maybe the easier way another way is
+
+00:39:16.240 --> 00:39:21.520
+through like templatic generation like
+
+00:39:18.160 --> 00:39:23.200
+this where you generate placeholders uh
+
+00:39:21.520 --> 00:39:25.200
+for all the information you want to add
+
+00:39:23.200 --> 00:39:26.480
+and then you add the information uh
+
+00:39:25.200 --> 00:39:29.359
+directly from the knowledge base through
+
+00:39:26.480 --> 00:39:29.359
+the placeholders like
+
+00:39:30.680 --> 00:39:36.800
+cool um there there's details about this
+
+00:39:34.240 --> 00:39:38.920
+in the paper like how we um formulate a
+
+00:39:36.800 --> 00:39:41.319
+training objective for something like
+
+00:39:38.920 --> 00:39:43.480
+this and the difficulty in formulating a
+
+00:39:41.319 --> 00:39:46.400
+training objective is that you need to
+
+00:39:43.480 --> 00:39:48.280
+figure out when you want to replace
+
+00:39:46.400 --> 00:39:49.720
+things so like you might not always want
+
+00:39:48.280 --> 00:39:51.000
+to replace with birth name you might
+
+00:39:49.720 --> 00:39:53.920
+want to replace with given name and
+
+00:39:51.000 --> 00:39:55.839
+family name and we demonstrate that you
+
+00:39:53.920 --> 00:39:58.400
+can figure out how to do this by
+
+00:39:55.839 --> 00:40:00.960
+essentially like Mar iing over the
+
+00:39:58.400 --> 00:40:03.520
+various ways of uh of doing this but
+
+00:40:00.960 --> 00:40:05.880
+that's kind of more complex detail
+
+00:40:03.520 --> 00:40:05.880
+that's in the
+
+00:40:08.440 --> 00:40:15.480
+paper another really interesting
+
+00:40:11.000 --> 00:40:17.319
+question um that uh we this is a also a
+
+00:40:15.480 --> 00:40:19.440
+paper that I was involved in from uh
+
+00:40:17.319 --> 00:40:22.040
+four years ago but I feel like this is
+
+00:40:19.440 --> 00:40:25.040
+not entirely solved even in like modern
+
+00:40:22.040 --> 00:40:26.920
+rag systems uh today is how can we
+
+00:40:25.040 --> 00:40:28.880
+reason over a lot of text that's
+
+00:40:26.920 --> 00:40:32.440
+included in a knowledge
+
+00:40:28.880 --> 00:40:35.839
+base um oh sorry reason over Text corpus
+
+00:40:32.440 --> 00:40:40.480
+like we reason over knowledge bases
+
+00:40:35.839 --> 00:40:43.280
+and basically uh what we did was we
+
+00:40:40.480 --> 00:40:44.960
+answered questions using text corpora as
+
+00:40:43.280 --> 00:40:48.680
+a traceable knowledge
+
+00:40:44.960 --> 00:40:52.800
+bases and we did relevance matching over
+
+00:40:48.680 --> 00:40:54.920
+mentions um and the way we did this is
+
+00:40:52.800 --> 00:40:57.440
+we created mentioned
+
+00:40:54.920 --> 00:40:59.480
+vectors and the mentioned vectors
+
+00:40:57.440 --> 00:41:01.720
+vectors of all of the mentions in the
+
+00:40:59.480 --> 00:41:04.920
+knowledge base of particular
+
+00:41:01.720 --> 00:41:05.920
+entities um and then we retrieved
+
+00:41:04.920 --> 00:41:09.599
+relevant
+
+00:41:05.920 --> 00:41:13.440
+mentions um from pre-trained Models uh
+
+00:41:09.599 --> 00:41:15.040
+so we we ran embeddings and generated uh
+
+00:41:13.440 --> 00:41:16.000
+embeddings for each of the mentions in
+
+00:41:15.040 --> 00:41:20.440
+the whole
+
+00:41:16.000 --> 00:41:25.440
+Corpus and based on this let let
+
+00:41:20.440 --> 00:41:29.119
+me find the place over here so based on
+
+00:41:25.440 --> 00:41:32.720
+this we basically um encoded all of
+
+00:41:29.119 --> 00:41:35.040
+these uh in here and then we had a dense
+
+00:41:32.720 --> 00:41:37.359
+query vector and the dense query Vector
+
+00:41:35.040 --> 00:41:41.640
+was specifically trained so that it
+
+00:41:37.359 --> 00:41:44.280
+would be able to identify entity
+
+00:41:41.640 --> 00:41:46.760
+mentions that answered the problem so if
+
+00:41:44.280 --> 00:41:50.240
+we had like when was The Grateful Dead
+
+00:41:46.760 --> 00:41:52.520
+and uh Bob Dylan album released uh we
+
+00:41:50.240 --> 00:41:54.760
+would have Bob Dylan be one vector The
+
+00:41:52.520 --> 00:41:56.560
+Grateful Dead be another vector and the
+
+00:41:54.760 --> 00:41:58.200
+model would be specifically trained so
+
+00:41:56.560 --> 00:42:00.040
+that when you took took the entity
+
+00:41:58.200 --> 00:42:03.319
+embedding of this and matched it with an
+
+00:42:00.040 --> 00:42:05.400
+entity embedding in this big Corpus of
+
+00:42:03.319 --> 00:42:07.920
+encoded things here it would be most
+
+00:42:05.400 --> 00:42:10.400
+likely to return relevant information to
+
+00:42:07.920 --> 00:42:13.160
+answer these like entity relation
+
+00:42:10.400 --> 00:42:14.680
+questions so then the question is how do
+
+00:42:13.160 --> 00:42:18.040
+we train a model like this how do we
+
+00:42:14.680 --> 00:42:20.280
+train like a dense uh embedding model so
+
+00:42:18.040 --> 00:42:21.520
+that it gets relevant information for
+
+00:42:20.280 --> 00:42:23.800
+answering
+
+00:42:21.520 --> 00:42:26.920
+questions and basically the way we did
+
+00:42:23.800 --> 00:42:29.280
+this was through week supervision uh
+
+00:42:26.920 --> 00:42:31.640
+just like I talked about for relation
+
+00:42:29.280 --> 00:42:33.599
+extraction in relation extraction we can
+
+00:42:31.640 --> 00:42:35.680
+create weak supervision by taking a big
+
+00:42:33.599 --> 00:42:37.960
+existing knowledge base and identifying
+
+00:42:35.680 --> 00:42:40.920
+all of the sentences where the answer is
+
+00:42:37.960 --> 00:42:43.319
+included and so what we did is we took
+
+00:42:40.920 --> 00:42:45.880
+this big existing knowledge base and
+
+00:42:43.319 --> 00:42:47.920
+said okay what are some of the relations
+
+00:42:45.880 --> 00:42:49.800
+in the knowledge base one example of a
+
+00:42:47.920 --> 00:42:51.559
+relation in the knowledge base is Steven
+
+00:42:49.800 --> 00:42:54.359
+Spielberg is the director of Saving
+
+00:42:51.559 --> 00:42:57.319
+Private Ryan so we created questions
+
+00:42:54.359 --> 00:42:59.119
+that said um
+
+00:42:57.319 --> 00:43:01.079
+was the director of Saving Private Ryan
+
+00:42:59.119 --> 00:43:03.920
+we can create those with templates uh
+
+00:43:01.079 --> 00:43:06.359
+easily for many different relations and
+
+00:43:03.920 --> 00:43:09.480
+then we took the embedding for Saving
+
+00:43:06.359 --> 00:43:10.760
+Private Ryan in that question and we
+
+00:43:09.480 --> 00:43:14.200
+tried to
+
+00:43:10.760 --> 00:43:17.119
+upweight all of the Saving Private Ryan
+
+00:43:14.200 --> 00:43:19.680
+embeddings over all of Wikipedia where
+
+00:43:17.119 --> 00:43:23.160
+Steven Spielberg cooccurred in that
+
+00:43:19.680 --> 00:43:25.640
+sentence so that tries to match um you
+
+00:43:23.160 --> 00:43:27.079
+know artificially created questions with
+
+00:43:25.640 --> 00:43:29.040
+sentences that would be the answer
+
+00:43:27.079 --> 00:43:31.040
+answer to that question and so that
+
+00:43:29.040 --> 00:43:32.480
+gives you like supervision it gives you
+
+00:43:31.040 --> 00:43:35.079
+a lot of data to train over it gives you
+
+00:43:32.480 --> 00:43:38.920
+a good model so that that allowed us to
+
+00:43:35.079 --> 00:43:41.319
+learn this model well so um this is one
+
+00:43:38.920 --> 00:43:43.160
+example of how you can do like rag spe
+
+00:43:41.319 --> 00:43:46.200
+specifically like informed by knowledge
+
+00:43:43.160 --> 00:43:46.200
+bases and stuff like
+
+00:43:47.280 --> 00:43:52.160
+that um any any questions about this
+
+00:43:53.480 --> 00:43:57.680
+or
+
+00:43:55.079 --> 00:44:00.079
+okay so another thing that I I'd like to
+
+00:43:57.680 --> 00:44:03.599
+go into is uh something we call schema
+
+00:44:00.079 --> 00:44:06.240
+free extraction and so if I go back to
+
+00:44:03.599 --> 00:44:09.960
+the wiki Data
+
+00:44:06.240 --> 00:44:10.760
+Page um Wiki data has something we call
+
+00:44:09.960 --> 00:44:13.599
+a
+
+00:44:10.760 --> 00:44:16.880
+schema and the schema is basically like
+
+00:44:13.599 --> 00:44:19.640
+what are the relations that are included
+
+00:44:16.880 --> 00:44:21.000
+in the database so one of the relations
+
+00:44:19.640 --> 00:44:25.079
+that's included in the databas is
+
+00:44:21.000 --> 00:44:25.079
+instance of I guess also
+
+00:44:25.200 --> 00:44:29.040
+image lots of images
+
+00:44:29.079 --> 00:44:33.880
+um
+
+00:44:30.440 --> 00:44:35.680
+signature uh sex or gender country of
+
+00:44:33.880 --> 00:44:38.319
+citizenship and these relations are like
+
+00:44:35.680 --> 00:44:41.079
+decided a priori by the people who
+
+00:44:38.319 --> 00:44:43.200
+created Wiki data um and there's lots
+
+00:44:41.079 --> 00:44:45.880
+and lots of them but that doesn't
+
+00:44:43.200 --> 00:44:48.880
+necessarily mean
+
+00:44:45.880 --> 00:44:50.400
+that like similarly to the problem of
+
+00:44:48.880 --> 00:44:51.839
+not having all of the entities we can't
+
+00:44:50.400 --> 00:44:55.119
+have all of the relations and just to
+
+00:44:51.839 --> 00:44:57.280
+give one example I was um in preparation
+
+00:44:55.119 --> 00:44:59.680
+for our large language models lecture I
+
+00:44:57.280 --> 00:45:02.640
+actually created some structured data
+
+00:44:59.680 --> 00:45:04.319
+about large language models and some of
+
+00:45:02.640 --> 00:45:06.119
+the instru the structured data about
+
+00:45:04.319 --> 00:45:09.319
+large language models that I created was
+
+00:45:06.119 --> 00:45:11.440
+like what is the variety of positional
+
+00:45:09.319 --> 00:45:13.079
+embedding that they're using or
+
+00:45:11.440 --> 00:45:15.800
+positional embedding variety and
+
+00:45:13.079 --> 00:45:18.720
+positional embedding variety is not in
+
+00:45:15.800 --> 00:45:20.359
+Wiki data I think um I'd be surprised if
+
+00:45:18.720 --> 00:45:23.200
+it was in Wiki data but I think it's not
+
+00:45:20.359 --> 00:45:25.760
+in Wiki data um so like as you go down
+
+00:45:23.200 --> 00:45:27.760
+to like more esoteric Concepts or like
+
+00:45:25.760 --> 00:45:29.599
+specialized domains or stuff like that
+
+00:45:27.760 --> 00:45:31.359
+you're almost always guaranteed to not
+
+00:45:29.599 --> 00:45:34.040
+you know have all the entities you need
+
+00:45:31.359 --> 00:45:36.680
+or not have all the relations you need
+
+00:45:34.040 --> 00:45:38.160
+so that's the problem that schema free
+
+00:45:36.680 --> 00:45:39.920
+extraction is trying to solve it's
+
+00:45:38.160 --> 00:45:41.680
+trying to figure out how we can like
+
+00:45:39.920 --> 00:45:45.920
+jointly figure out the schema together
+
+00:45:41.680 --> 00:45:45.920
+with uh the information you want to
+
+00:45:48.480 --> 00:45:54.040
+extract and the um the most famous
+
+00:45:52.319 --> 00:45:55.599
+example of this is something called open
+
+00:45:54.040 --> 00:45:57.200
+information extraction in open
+
+00:45:55.599 --> 00:46:01.160
+information extraction basically what
+
+00:45:57.200 --> 00:46:04.040
+it's saying is um we don't need a schema
+
+00:46:01.160 --> 00:46:06.359
+uh there's no there's no schema um the
+
+00:46:04.040 --> 00:46:08.720
+only schema that we have is the actual
+
+00:46:06.359 --> 00:46:12.200
+text in the sentences that we're
+
+00:46:08.720 --> 00:46:14.520
+referring to um the entities so if we
+
+00:46:12.200 --> 00:46:16.040
+have United United has a Hub in Chicago
+
+00:46:14.520 --> 00:46:17.359
+which is the headquarters of United
+
+00:46:16.040 --> 00:46:21.200
+Continental
+
+00:46:17.359 --> 00:46:25.880
+Holdings um the relation is literally
+
+00:46:21.200 --> 00:46:29.359
+has a Hub in um that that's the relation
+
+00:46:25.880 --> 00:46:33.359
+um and then for this we have Chicago is
+
+00:46:29.359 --> 00:46:35.559
+the headquarters of um but the problem
+
+00:46:33.359 --> 00:46:37.520
+with this uh is that this cannot
+
+00:46:35.559 --> 00:46:40.359
+abstract away so if we had another
+
+00:46:37.520 --> 00:46:42.000
+sentence that said Chicago or United
+
+00:46:40.359 --> 00:46:44.319
+Continental Holdings has its
+
+00:46:42.000 --> 00:46:45.720
+headquarters in Chicago that would be
+
+00:46:44.319 --> 00:46:49.800
+treated as completely different you
+
+00:46:45.720 --> 00:46:49.800
+wouldn't be able to like group those two
+
+00:46:51.119 --> 00:46:57.720
+together so um in open information
+
+00:46:55.000 --> 00:47:00.079
+extraction actually a lot of the methods
+
+00:46:57.720 --> 00:47:02.800
+this is one of the few things where
+
+00:47:00.079 --> 00:47:05.480
+people still use rule-based systems as
+
+00:47:02.800 --> 00:47:07.640
+kind of like uh you know almost
+
+00:47:05.480 --> 00:47:09.319
+state-of-the-art systems but basically
+
+00:47:07.640 --> 00:47:11.559
+the reason why you're able to do this is
+
+00:47:09.319 --> 00:47:14.440
+it's not actually that hard to extract
+
+00:47:11.559 --> 00:47:16.839
+kind of the relevant strings between uh
+
+00:47:14.440 --> 00:47:19.599
+two entities and so the both the
+
+00:47:16.839 --> 00:47:21.359
+Precision and recall are pretty high and
+
+00:47:19.599 --> 00:47:24.079
+another reason why people use rule-based
+
+00:47:21.359 --> 00:47:25.760
+systems is because they um like you want
+
+00:47:24.079 --> 00:47:27.440
+to run it over the whole web and running
+
+00:47:25.760 --> 00:47:29.079
+a neural model over the whole web is
+
+00:47:27.440 --> 00:47:32.000
+expensive so you can use a role-based
+
+00:47:29.079 --> 00:47:35.319
+model so some examples of this include
+
+00:47:32.000 --> 00:47:37.640
+text Runner and Reverb um the basic
+
+00:47:35.319 --> 00:47:41.000
+ideas behind them is that you use a
+
+00:47:37.640 --> 00:47:43.720
+parser to extract um to do a syntactic
+
+00:47:41.000 --> 00:47:45.760
+analysis of the sentence um in extract
+
+00:47:43.720 --> 00:47:47.640
+during according to rules so for example
+
+00:47:45.760 --> 00:47:50.160
+the relation must contain a
+
+00:47:47.640 --> 00:47:52.720
+predicate um the subject and object must
+
+00:47:50.160 --> 00:47:56.040
+be noun phrases other things like
+
+00:47:52.720 --> 00:47:57.640
+this um and then what they did later is
+
+00:47:56.040 --> 00:47:59.240
+what they did in this this paper
+
+00:47:57.640 --> 00:48:00.800
+arguably this is maybe no longer
+
+00:47:59.240 --> 00:48:02.280
+necessary with the compute power we have
+
+00:48:00.800 --> 00:48:04.000
+now but they trained an even faster
+
+00:48:02.280 --> 00:48:06.960
+model to extract over large amounts of
+
+00:48:04.000 --> 00:48:08.720
+data so they basically um use this as a
+
+00:48:06.960 --> 00:48:10.599
+su weak supervision and then train a
+
+00:48:08.720 --> 00:48:12.160
+model that could do it even faster with
+
+00:48:10.599 --> 00:48:14.680
+the sequence base
+
+00:48:12.160 --> 00:48:18.119
+model
+
+00:48:14.680 --> 00:48:19.880
+um another thing that they did was um
+
+00:48:18.119 --> 00:48:22.280
+they aggregated multiple pieces of
+
+00:48:19.880 --> 00:48:24.480
+evidence heris to find common and
+
+00:48:22.280 --> 00:48:28.760
+therefore potentially reliable
+
+00:48:24.480 --> 00:48:28.760
+extractions so like
+
+00:48:29.800 --> 00:48:36.960
+any piece of text on the internet like
+
+00:48:31.559 --> 00:48:40.200
+could be a lie right so um you know
+
+00:48:36.960 --> 00:48:43.400
+if I I might write on my blog United has
+
+00:48:40.200 --> 00:48:45.119
+a Hub in like Denver or on the other
+
+00:48:43.400 --> 00:48:48.240
+hand
+
+00:48:45.119 --> 00:48:50.839
+um wait a set
+
+00:48:48.240 --> 00:48:52.680
+right some something has a Hub in Denver
+
+00:48:50.839 --> 00:48:54.960
+but United has a Hub in Pittsburgh is
+
+00:48:52.680 --> 00:48:58.040
+definitely wrong so let's uh let's go
+
+00:48:54.960 --> 00:49:00.000
+with that um uh so somebody could write
+
+00:48:58.040 --> 00:49:02.359
+that on the internet and in fact because
+
+00:49:00.000 --> 00:49:06.440
+I just said it it's probably in YouTube
+
+00:49:02.359 --> 00:49:09.119
+comments somewhere but um uh
+
+00:49:06.440 --> 00:49:10.760
+like any any piece of information on the
+
+00:49:09.119 --> 00:49:13.079
+internet could be wrong so basically
+
+00:49:10.760 --> 00:49:16.680
+they had um heuristic methods to filter
+
+00:49:13.079 --> 00:49:19.559
+these out and usually these were
+
+00:49:16.680 --> 00:49:21.559
+frequency based so it's like um if both
+
+00:49:19.559 --> 00:49:23.520
+United and Pittsburgh are very common
+
+00:49:21.559 --> 00:49:26.000
+but it's very rare for somebody to says
+
+00:49:23.520 --> 00:49:27.799
+say United has a Hub in Pittsburgh then
+
+00:49:26.000 --> 00:49:29.200
+that means it's statistically unlikely
+
+00:49:27.799 --> 00:49:30.799
+for this to be correct because if it
+
+00:49:29.200 --> 00:49:33.280
+were correct we'd expect to see it much
+
+00:49:30.799 --> 00:49:36.799
+more frequently so um those were the
+
+00:49:33.280 --> 00:49:36.799
+kind of things that they they did
+
+00:49:37.520 --> 00:49:44.440
+here there's also some neural models for
+
+00:49:40.400 --> 00:49:46.839
+open IE um I I think these are uh used
+
+00:49:44.440 --> 00:49:48.440
+maybe a little bit less often um but
+
+00:49:46.839 --> 00:49:52.559
+basically heuristics are still not
+
+00:49:48.440 --> 00:49:55.280
+perfect and so what they did the problem
+
+00:49:52.559 --> 00:49:56.720
+with um like not relying on heuristics
+
+00:49:55.280 --> 00:49:58.880
+is you need to get training data from
+
+00:49:56.720 --> 00:50:01.880
+somewhere so there's a rather clever
+
+00:49:58.880 --> 00:50:03.599
+paper um and again if you're not
+
+00:50:01.880 --> 00:50:05.119
+interested in relation extraction in
+
+00:50:03.599 --> 00:50:07.559
+particular I think this is one thing
+
+00:50:05.119 --> 00:50:10.000
+that's still worth paying attention to
+
+00:50:07.559 --> 00:50:12.680
+um which is
+
+00:50:10.000 --> 00:50:14.559
+they demonstrated that it's possible to
+
+00:50:12.680 --> 00:50:16.319
+create relatively large data sets by
+
+00:50:14.559 --> 00:50:18.160
+asking people simple
+
+00:50:16.319 --> 00:50:21.440
+questions
+
+00:50:18.160 --> 00:50:24.480
+and in particular they wanted to
+
+00:50:21.440 --> 00:50:27.119
+get relation extraction data sets that
+
+00:50:24.480 --> 00:50:30.799
+are like um
+
+00:50:27.119 --> 00:50:34.200
+who finished something like UCD finished
+
+00:50:30.799 --> 00:50:37.760
+the two 2006 championships and if you
+
+00:50:34.200 --> 00:50:40.720
+ask people like okay select this span um
+
+00:50:37.760 --> 00:50:44.559
+select the entity span the relations
+
+00:50:40.720 --> 00:50:46.160
+span and the um in the second entity the
+
+00:50:44.559 --> 00:50:49.079
+head entity the relation and the tail
+
+00:50:46.160 --> 00:50:51.839
+entity select it on this interface and
+
+00:50:49.079 --> 00:50:54.200
+then uh tell me is it this relation or
+
+00:50:51.839 --> 00:50:55.640
+this relation or this relation that's
+
+00:50:54.200 --> 00:50:58.160
+actually pretty hard and getting like
+
+00:50:55.640 --> 00:51:01.280
+crowd workers to start learning how to
+
+00:50:58.160 --> 00:51:03.280
+do that task is a bit tricky and it
+
+00:51:01.280 --> 00:51:06.400
+takes some you know it takes some time
+
+00:51:03.280 --> 00:51:07.799
+to get them onboarded basically um but
+
+00:51:06.400 --> 00:51:09.760
+basically what they said is instead
+
+00:51:07.799 --> 00:51:11.359
+we'll just ask them questions where the
+
+00:51:09.760 --> 00:51:14.240
+answer to the question basically gives
+
+00:51:11.359 --> 00:51:17.160
+us the answer to what the relation is so
+
+00:51:14.240 --> 00:51:20.319
+they ask like who finished something and
+
+00:51:17.160 --> 00:51:23.680
+the answer is like UCD and um what did
+
+00:51:20.319 --> 00:51:25.359
+someone finish the 2006 Championship
+
+00:51:23.680 --> 00:51:28.920
+what did someone fish some finish
+
+00:51:25.359 --> 00:51:31.760
+something as and basically um in doing
+
+00:51:28.920 --> 00:51:33.319
+this they created uh something called
+
+00:51:31.760 --> 00:51:34.359
+semantic roles which we're actually
+
+00:51:33.319 --> 00:51:35.960
+probably going to talk about a little
+
+00:51:34.359 --> 00:51:37.559
+bit later but you can take the semantic
+
+00:51:35.960 --> 00:51:41.200
+roles and then you can use them to
+
+00:51:37.559 --> 00:51:43.920
+annotate uh relation extraction data and
+
+00:51:41.200 --> 00:51:46.720
+then they trained a supervised neural
+
+00:51:43.920 --> 00:51:46.720
+tager for
+
+00:51:48.799 --> 00:51:53.480
+this
+
+00:51:50.480 --> 00:51:56.040
+cool um so another thing I'd like to
+
+00:51:53.480 --> 00:51:57.880
+talk about is I talked about learning um
+
+00:51:56.040 --> 00:51:59.920
+information about entities from entity
+
+00:51:57.880 --> 00:52:02.079
+embeddings but you can actually learn
+
+00:51:59.920 --> 00:52:04.520
+information about relations from
+
+00:52:02.079 --> 00:52:07.680
+relation information about other
+
+00:52:04.520 --> 00:52:12.359
+relations and this can help solve the
+
+00:52:07.680 --> 00:52:16.119
+problem um of like essentially the fact
+
+00:52:12.359 --> 00:52:18.760
+that open IE is not able to abstract and
+
+00:52:16.119 --> 00:52:20.680
+generalize so word embeddings or entity
+
+00:52:18.760 --> 00:52:23.079
+embeddings give information of the word
+
+00:52:20.680 --> 00:52:26.920
+in context um which can be indicative
+
+00:52:23.079 --> 00:52:29.640
+for knowledge uh knowledge bases
+
+00:52:26.920 --> 00:52:32.640
+but other relations or combinations
+
+00:52:29.640 --> 00:52:34.960
+thereof are also indicative of them and
+
+00:52:32.640 --> 00:52:36.960
+um if anybody is familiar with graphs or
+
+00:52:34.960 --> 00:52:39.520
+graph processing there's the whole idea
+
+00:52:36.960 --> 00:52:41.400
+of um link prediction where you're given
+
+00:52:39.520 --> 00:52:42.680
+like a a small number of links in a
+
+00:52:41.400 --> 00:52:45.760
+graph and you want to predict what other
+
+00:52:42.680 --> 00:52:50.559
+links are likely to uh
+
+00:52:45.760 --> 00:52:52.920
+exist and like as I said um a lot of uh
+
+00:52:50.559 --> 00:52:54.839
+you know very prominent AI researchers
+
+00:52:52.920 --> 00:52:57.440
+got their start in uh relation
+
+00:52:54.839 --> 00:53:01.480
+extraction and uh it sker is another one
+
+00:52:57.440 --> 00:53:04.319
+of them actually um and uh basically
+
+00:53:01.480 --> 00:53:07.880
+this 2009 paper proposed to use tensor
+
+00:53:04.319 --> 00:53:09.400
+de composition to do uh induction of
+
+00:53:07.880 --> 00:53:13.520
+relations
+
+00:53:09.400 --> 00:53:15.319
+and the way it worked is um you model
+
+00:53:13.520 --> 00:53:18.400
+relations by decomposing a tensor
+
+00:53:15.319 --> 00:53:21.599
+containing entity relation entity tles
+
+00:53:18.400 --> 00:53:24.000
+so you have the left entity the right
+
+00:53:21.599 --> 00:53:27.160
+entity and whether the relation exists
+
+00:53:24.000 --> 00:53:31.319
+is this big um uh big tensor in the
+
+00:53:27.160 --> 00:53:33.160
+Middle where these are embeddings of the
+
+00:53:31.319 --> 00:53:35.760
+left entity these are embeddings of the
+
+00:53:33.160 --> 00:53:38.839
+right entity and then the the depth of
+
+00:53:35.760 --> 00:53:40.680
+the tensor is like which relations exist
+
+00:53:38.839 --> 00:53:43.760
+and so we know that some exist so we
+
+00:53:40.680 --> 00:53:46.640
+give them a one we know others exist um
+
+00:53:43.760 --> 00:53:48.680
+don't exist so we give them a zero um
+
+00:53:46.640 --> 00:53:51.040
+and then we do a low rank approximation
+
+00:53:48.680 --> 00:53:52.559
+of this tensor and if we do a low rank
+
+00:53:51.040 --> 00:53:55.720
+approximation of the tensor we have
+
+00:53:52.559 --> 00:53:57.280
+reconstruction ER basically so when we
+
+00:53:55.720 --> 00:53:59.960
+reconstruct the are some things that
+
+00:53:57.280 --> 00:54:01.960
+were previously zero become one and so
+
+00:53:59.960 --> 00:54:04.760
+the things that were previously zero and
+
+00:54:01.960 --> 00:54:07.880
+then become close to one are the ones
+
+00:54:04.760 --> 00:54:10.559
+that we think like actually might exist
+
+00:54:07.880 --> 00:54:12.000
+they might be real um they might be real
+
+00:54:10.559 --> 00:54:13.640
+relations that we were just missing
+
+00:54:12.000 --> 00:54:16.599
+because our previous knowledge base was
+
+00:54:13.640 --> 00:54:16.599
+complete uh
+
+00:54:18.640 --> 00:54:26.880
+incomplete and um one thing that takes
+
+00:54:21.799 --> 00:54:28.559
+us a step further is uh what if if we
+
+00:54:26.880 --> 00:54:30.079
+actually do have a knowledge basee or
+
+00:54:28.559 --> 00:54:31.839
+what if we even have multiple knowledge
+
+00:54:30.079 --> 00:54:35.520
+bases like what if we have Wiki data and
+
+00:54:31.839 --> 00:54:36.640
+we have wordnet and we have um uh other
+
+00:54:35.520 --> 00:54:38.920
+things like
+
+00:54:36.640 --> 00:54:40.680
+this and in addition to that we also
+
+00:54:38.920 --> 00:54:43.400
+have open IE
+
+00:54:40.680 --> 00:54:45.960
+extractions so there's an idea of
+
+00:54:43.400 --> 00:54:47.880
+something called Universal schema and
+
+00:54:45.960 --> 00:54:50.200
+what Universal schema do is they embed
+
+00:54:47.880 --> 00:54:55.119
+relations from multiple schema or
+
+00:54:50.200 --> 00:54:56.960
+schemata in the same space and based on
+
+00:54:55.119 --> 00:54:59.559
+this they then
+
+00:54:56.960 --> 00:55:01.359
+predict which ones exist are likely to
+
+00:54:59.559 --> 00:55:04.400
+exist or which ones are not likely to
+
+00:55:01.359 --> 00:55:06.680
+exist so here we might have a free base
+
+00:55:04.400 --> 00:55:08.640
+or Wiki data we might have another uh
+
+00:55:06.680 --> 00:55:11.559
+kind of relation extraction data set
+
+00:55:08.640 --> 00:55:15.480
+called Tac and then on the training data
+
+00:55:11.559 --> 00:55:17.040
+set we have um like all of these uh
+
+00:55:15.480 --> 00:55:20.240
+things that are like positive or
+
+00:55:17.040 --> 00:55:23.960
+negative or something like this and then
+
+00:55:20.240 --> 00:55:26.960
+on the heldout data set we have only
+
+00:55:23.960 --> 00:55:29.480
+information about like open
+
+00:55:26.960 --> 00:55:30.920
+for example so um for all of the
+
+00:55:29.480 --> 00:55:33.079
+entities that exist in the knowledge
+
+00:55:30.920 --> 00:55:34.839
+base we know you know whether the
+
+00:55:33.079 --> 00:55:36.039
+relations exist for but for all the
+
+00:55:34.839 --> 00:55:39.640
+entities that don't exist in the
+
+00:55:36.039 --> 00:55:41.760
+database we don't know and so uh then
+
+00:55:39.640 --> 00:55:43.839
+just from the existence of open IE
+
+00:55:41.760 --> 00:55:45.480
+relations or non-existence of open IE
+
+00:55:43.839 --> 00:55:47.920
+relations we can predict that other
+
+00:55:45.480 --> 00:55:49.359
+relations might exist for example so
+
+00:55:47.920 --> 00:55:51.079
+this is a great way to combine the two
+
+00:55:49.359 --> 00:55:53.920
+together like open IE you can run it
+
+00:55:51.079 --> 00:55:55.880
+over you know very large data sets um
+
+00:55:53.920 --> 00:55:58.000
+but it doesn't have a good schema free
+
+00:55:55.880 --> 00:56:00.400
+uh Wiki data has a good schema but you
+
+00:55:58.000 --> 00:56:02.960
+can't you know it's all manually created
+
+00:56:00.400 --> 00:56:04.720
+so you can suggest other ones and one
+
+00:56:02.960 --> 00:56:07.960
+other like interesting thing is you can
+
+00:56:04.720 --> 00:56:09.640
+suggest other um things that might exist
+
+00:56:07.960 --> 00:56:13.039
+in Wiki data but you could also track
+
+00:56:09.640 --> 00:56:15.039
+that back to the original text that
+
+00:56:13.039 --> 00:56:17.000
+indicated that it might exist in Wiki
+
+00:56:15.039 --> 00:56:18.720
+data so then you could have a human go
+
+00:56:17.000 --> 00:56:20.520
+back and check it to make sure that
+
+00:56:18.720 --> 00:56:24.200
+that's actually true and trustworthy and
+
+00:56:20.520 --> 00:56:24.200
+other things like that
+
+00:56:26.400 --> 00:56:31.400
+cool um so if you like uh you like
+
+00:56:29.400 --> 00:56:33.160
+tensors or you like linear algebra or
+
+00:56:31.400 --> 00:56:34.720
+things like this this is maybe something
+
+00:56:33.160 --> 00:56:37.880
+that you could take a look at and think
+
+00:56:34.720 --> 00:56:40.240
+a little bit more about um any any
+
+00:56:37.880 --> 00:56:40.240
+questions
+
+00:56:42.799 --> 00:56:46.240
+here okay
+
+00:56:46.880 --> 00:56:53.680
+cool um so another thing I'd like to
+
+00:56:50.640 --> 00:56:56.920
+talk about is uh modeling relation paths
+
+00:56:53.680 --> 00:57:00.359
+so this is a really nice uh idea
+
+00:56:56.920 --> 00:57:00.359
+which is you
+
+00:57:00.440 --> 00:57:05.000
+can make inferences across multiple hops
+
+00:57:04.240 --> 00:57:08.400
+of
+
+00:57:05.000 --> 00:57:12.280
+relations um based on uh particular
+
+00:57:08.400 --> 00:57:14.200
+relations existing and so um multi-step
+
+00:57:12.280 --> 00:57:17.280
+passs can be informative for indicating
+
+00:57:14.200 --> 00:57:20.000
+whether individual relations exist so um
+
+00:57:17.280 --> 00:57:24.400
+for example uh given a word given a
+
+00:57:20.000 --> 00:57:27.960
+particular word in a paper title
+
+00:57:24.400 --> 00:57:29.880
+recommend a venue in which to the paper
+
+00:57:27.960 --> 00:57:32.559
+and so this is the the problem that they
+
+00:57:29.880 --> 00:57:36.079
+were trying to solve and then basically
+
+00:57:32.559 --> 00:57:38.440
+you have a word um you
+
+00:57:36.079 --> 00:57:41.119
+find if you have that word in your paper
+
+00:57:38.440 --> 00:57:42.920
+title you then find other papers that
+
+00:57:41.119 --> 00:57:45.280
+have that title uh that have that word
+
+00:57:42.920 --> 00:57:48.359
+in their title and those papers are in a
+
+00:57:45.280 --> 00:57:52.039
+journal and that gets a high weight with
+
+00:57:48.359 --> 00:57:54.119
+respect to like that your paper being
+
+00:57:52.039 --> 00:57:56.839
+you know relevant to that particular
+
+00:57:54.119 --> 00:57:59.880
+Journal you can also say
+
+00:57:56.839 --> 00:58:01.000
+okay I have a a word find papers with
+
+00:57:59.880 --> 00:58:03.240
+that word in the
+
+00:58:01.000 --> 00:58:07.240
+title find the first author of that
+
+00:58:03.240 --> 00:58:09.280
+paper find another paper uh that had
+
+00:58:07.240 --> 00:58:11.599
+that author as a first author and then
+
+00:58:09.280 --> 00:58:13.240
+find the Journal of it and they
+
+00:58:11.599 --> 00:58:15.839
+demonstrate a way where you can like
+
+00:58:13.240 --> 00:58:18.280
+expand these paths and feed them into a
+
+00:58:15.839 --> 00:58:22.400
+prediction model and use that to predict
+
+00:58:18.280 --> 00:58:25.480
+um you know additional relations so
+
+00:58:22.400 --> 00:58:26.680
+unlike this method here this method was
+
+00:58:25.480 --> 00:58:29.240
+saying like
+
+00:58:26.680 --> 00:58:30.920
+other single relations are indicative of
+
+00:58:29.240 --> 00:58:34.160
+a particular relation
+
+00:58:30.920 --> 00:58:36.880
+existing this paper is saying not just
+
+00:58:34.160 --> 00:58:38.720
+individual relations are indicative of
+
+00:58:36.880 --> 00:58:40.640
+another relation existing but actually
+
+00:58:38.720 --> 00:58:43.839
+relation paths are indicative of a
+
+00:58:40.640 --> 00:58:46.400
+relation existing so this is more um
+
+00:58:43.839 --> 00:58:46.400
+expressive
+
+00:58:47.520 --> 00:58:55.359
+basically um and this followup paper
+
+00:58:52.640 --> 00:58:57.480
+uh using differentiable logic rules
+
+00:58:55.359 --> 00:59:00.799
+actually made this endtoend
+
+00:58:57.480 --> 00:59:03.079
+trainable so this allows you to consider
+
+00:59:00.799 --> 00:59:07.599
+whole paths in a differentiable
+
+00:59:03.079 --> 00:59:09.960
+framework and so the way they did this
+
+00:59:07.599 --> 00:59:13.359
+is like if you have you know City in
+
+00:59:09.960 --> 00:59:16.440
+country and has office in country um
+
+00:59:13.359 --> 00:59:18.920
+that or sorry City and Country and has
+
+00:59:16.440 --> 00:59:22.200
+office in city that indicates has office
+
+00:59:18.920 --> 00:59:24.160
+in country and I I'm sure you know many
+
+00:59:22.200 --> 00:59:26.760
+people here have thought like learned
+
+00:59:24.160 --> 00:59:29.520
+about logic and you know and induction
+
+00:59:26.760 --> 00:59:32.720
+from or deduction from uh logic rules
+
+00:59:29.520 --> 00:59:34.359
+and stuff like this but the problem is
+
+00:59:32.720 --> 00:59:37.079
+deduction from logic rules is very
+
+00:59:34.359 --> 00:59:39.039
+fragile like there are cases where there
+
+00:59:37.079 --> 00:59:41.119
+are counter examples so if you say that
+
+00:59:39.039 --> 00:59:43.280
+something is always true deductively
+
+00:59:41.119 --> 00:59:45.839
+then um that can cause problems so in
+
+00:59:43.280 --> 00:59:47.839
+reality it's like if you have two pieces
+
+00:59:45.839 --> 00:59:52.400
+of information something can become much
+
+00:59:47.839 --> 00:59:56.920
+much more likely um and so you know just
+
+00:59:52.400 --> 00:59:59.880
+to give an example um somebody studying
+
+00:59:56.920 --> 01:00:01.280
+studying at CMU makes it very likely
+
+00:59:59.880 --> 01:00:03.799
+much more likely that they're studying
+
+01:00:01.280 --> 01:00:06.359
+computer science and much less likely
+
+01:00:03.799 --> 01:00:08.000
+that they're studying medicine or
+
+01:00:06.359 --> 01:00:09.520
+something like that but that doesn't
+
+01:00:08.000 --> 01:00:11.720
+mean that it like
+
+01:00:09.520 --> 01:00:13.559
+entirely the first one is definitely not
+
+01:00:11.720 --> 01:00:15.480
+entirely implied and I'm sure there's
+
+01:00:13.559 --> 01:00:16.760
+like a few people at CMU who are somehow
+
+01:00:15.480 --> 01:00:18.440
+studying medicine through a joint
+
+01:00:16.760 --> 01:00:21.480
+program with pit or something like that
+
+01:00:18.440 --> 01:00:24.400
+so you know like very it's very rare
+
+01:00:21.480 --> 01:00:26.799
+that logic rules are hard and fast and
+
+01:00:24.400 --> 01:00:28.480
+so basically what they do is they treat
+
+01:00:26.799 --> 01:00:30.559
+each path as a sequence of Matrix
+
+01:00:28.480 --> 01:00:34.839
+multiplies it where they have a rule
+
+01:00:30.559 --> 01:00:36.599
+weight um like this and um in the end
+
+01:00:34.839 --> 01:00:38.359
+that allows you to make a a prediction
+
+01:00:36.599 --> 01:00:40.839
+about whether a predic logic rule is
+
+01:00:38.359 --> 01:00:40.839
+correct or
+
+01:00:40.880 --> 01:00:49.319
+not um so this is uh i' I've been
+
+01:00:46.880 --> 01:00:51.119
+working mostly in like structured
+
+01:00:49.319 --> 01:00:54.480
+knowledge space structured knowledge
+
+01:00:51.119 --> 01:00:56.599
+graphs other uh other things like this
+
+01:00:54.480 --> 01:00:59.760
+um I I don't
+
+01:00:56.599 --> 01:01:02.720
+think there's a whole lot of work that
+
+01:00:59.760 --> 01:01:05.640
+directly applies this to language models
+
+01:01:02.720 --> 01:01:07.319
+um like differentiable logic rules and
+
+01:01:05.640 --> 01:01:10.079
+language models or things like that just
+
+01:01:07.319 --> 01:01:12.440
+because it's less clean it's you know uh
+
+01:01:10.079 --> 01:01:13.839
+harder um there there's a little bit of
+
+01:01:12.440 --> 01:01:16.079
+work which I'm going to talk about now
+
+01:01:13.839 --> 01:01:18.599
+but I think like this kind of work is
+
+01:01:16.079 --> 01:01:21.440
+interesting because a lot of models are
+
+01:01:18.599 --> 01:01:23.119
+not super great at reasoning and how to
+
+01:01:21.440 --> 01:01:25.119
+like allow them to be better at
+
+01:01:23.119 --> 01:01:26.559
+reasoning is kind of an open problem so
+
+01:01:25.119 --> 01:01:28.039
+learning from these old older works that
+
+01:01:26.559 --> 01:01:30.200
+did it in a more structured space and
+
+01:01:28.039 --> 01:01:32.160
+trying to figure out how to apply them
+
+01:01:30.200 --> 01:01:34.400
+to less structured spaces is still
+
+01:01:32.160 --> 01:01:36.240
+interesting I think
+
+01:01:34.400 --> 01:01:39.160
+so
+
+01:01:36.240 --> 01:01:40.720
+cool um then the final talk topic I want
+
+01:01:39.160 --> 01:01:42.920
+to talk about is probing knowledge in
+
+01:01:40.720 --> 01:01:44.920
+LMS and so we have these knowledge bases
+
+01:01:42.920 --> 01:01:47.319
+that encode you know tons and tons of
+
+01:01:44.920 --> 01:01:49.880
+knowledge um which allows us to figure
+
+01:01:47.319 --> 01:01:52.200
+out you know oh well how well do uh
+
+01:01:49.880 --> 01:01:56.200
+language models know about these
+
+01:01:52.200 --> 01:01:59.079
+things and so
+
+01:01:56.200 --> 01:02:02.760
+traditional um kind of QA machine
+
+01:01:59.079 --> 01:02:04.799
+reading comprehension rag models um
+
+01:02:02.760 --> 01:02:06.359
+usually referred to external resources
+
+01:02:04.799 --> 01:02:10.039
+to answer questions like Wikipedia
+
+01:02:06.359 --> 01:02:14.359
+articles um or things like this but then
+
+01:02:10.039 --> 01:02:16.119
+the question is without doing rag can we
+
+01:02:14.359 --> 01:02:18.160
+you know answer questions like what
+
+01:02:16.119 --> 01:02:20.920
+knowledge is
+
+01:02:18.160 --> 01:02:24.079
+encoded and so the first paper that kind
+
+01:02:20.920 --> 01:02:26.520
+of handled this sort of problem uh is
+
+01:02:24.079 --> 01:02:29.200
+this paper which actually was also
+
+01:02:26.520 --> 01:02:33.359
+called uh
+
+01:02:29.200 --> 01:02:35.960
+wama surprisingly um or released a
+
+01:02:33.359 --> 01:02:41.000
+resource called llama except it was l m
+
+01:02:35.960 --> 01:02:44.880
+a um but what they did is they
+
+01:02:41.000 --> 01:02:46.960
+uh used they in contrast to using
+
+01:02:44.880 --> 01:02:50.000
+structural queries like SQL or or
+
+01:02:46.960 --> 01:02:52.119
+Sparkle two query KBS they tried to use
+
+01:02:50.000 --> 01:02:54.240
+natural language prompts to query LM so
+
+01:02:52.119 --> 01:02:58.160
+this was actually one of the the first
+
+01:02:54.240 --> 01:03:02.359
+uh kind of paper on prompts uh prompting
+
+01:02:58.160 --> 01:03:05.079
+for uh language models in a way and the
+
+01:03:02.359 --> 01:03:08.359
+way they did this is they had um they
+
+01:03:05.079 --> 01:03:10.039
+did like Dante was born in mask and then
+
+01:03:08.359 --> 01:03:13.279
+they tried to fill in the mask using a
+
+01:03:10.039 --> 01:03:15.839
+mask language model and uh and output
+
+01:03:13.279 --> 01:03:18.559
+Florence so
+
+01:03:15.839 --> 01:03:19.960
+um when they did this work now now we
+
+01:03:18.559 --> 01:03:21.359
+don't do this quite as much but when
+
+01:03:19.960 --> 01:03:23.520
+they did this work they basically used
+
+01:03:21.359 --> 01:03:25.440
+the knowledge base as the ground truth
+
+01:03:23.520 --> 01:03:28.880
+and tried to probe whether the knowledge
+
+01:03:25.440 --> 01:03:31.520
+in in um in the knowledge base was also
+
+01:03:28.880 --> 01:03:34.880
+uh recoverable from the neural
+
+01:03:31.520 --> 01:03:37.720
+map um and they proposed the Llama
+
+01:03:34.880 --> 01:03:39.760
+Benchmark um basically it was manual
+
+01:03:37.720 --> 01:03:42.480
+prompts for 41 relations they created
+
+01:03:39.760 --> 01:03:44.839
+the prompts manually uh so like X was
+
+01:03:42.480 --> 01:03:46.480
+founded in y The Prompt template and
+
+01:03:44.839 --> 01:03:49.400
+they filled in the subjects and had the
+
+01:03:46.480 --> 01:03:52.160
+LMS uh for such as Bert predict the
+
+01:03:49.400 --> 01:03:55.839
+objects uh like blueberg LP was founded
+
+01:03:52.160 --> 01:03:59.000
+in mask and they demonstrated that like
+
+01:03:55.839 --> 01:04:02.440
+basically Elmo uh Transformer XL and
+
+01:03:59.000 --> 01:04:04.960
+Bert base got uh you know up to 31%
+
+01:04:02.440 --> 01:04:06.480
+accuracy now I'm sure uh the modern
+
+01:04:04.960 --> 01:04:09.200
+language models would have much higher
+
+01:04:06.480 --> 01:04:11.279
+accuracy than
+
+01:04:09.200 --> 01:04:13.920
+that
+
+01:04:11.279 --> 01:04:17.839
+um this is a a follow-up paper that we
+
+01:04:13.920 --> 01:04:21.160
+did to this um where we tried to do this
+
+01:04:17.839 --> 01:04:23.400
+multilingually um I I think this is
+
+01:04:21.160 --> 01:04:25.680
+really let
+
+01:04:23.400 --> 01:04:29.520
+me I think one thing that's interesting
+
+01:04:25.680 --> 01:04:31.960
+interesting about this paper is um even
+
+01:04:29.520 --> 01:04:37.240
+if you're not interested in multilingual
+
+01:04:31.960 --> 01:04:38.920
+stuff per se there is an interesting
+
+01:04:37.240 --> 01:04:40.760
+dichotomy about like what knowledge is
+
+01:04:38.920 --> 01:04:43.079
+included in LMS and whether we can
+
+01:04:40.760 --> 01:04:46.000
+retrieve it and the reason why I'm
+
+01:04:43.079 --> 01:04:48.359
+saying this is because in this paper
+
+01:04:46.000 --> 01:04:51.200
+we created
+
+01:04:48.359 --> 01:04:52.599
+queries from a knowledge base and
+
+01:04:51.200 --> 01:04:54.160
+because we created queries from a
+
+01:04:52.599 --> 01:04:55.760
+knowledge base and knowledge bases are
+
+01:04:54.160 --> 01:04:57.240
+multilingual we can also create
+
+01:04:55.760 --> 01:05:00.039
+multilingual queries from knowledge
+
+01:04:57.240 --> 01:05:01.720
+bases right so we can use exactly the
+
+01:05:00.039 --> 01:05:03.359
+same entities but just ask the same
+
+01:05:01.720 --> 01:05:05.920
+question in different languages and so
+
+01:05:03.359 --> 01:05:07.480
+we had a bunch of people manually uh
+
+01:05:05.920 --> 01:05:10.119
+create prompts for all of these
+
+01:05:07.480 --> 01:05:13.000
+languages here and you can see that in
+
+01:05:10.119 --> 01:05:15.960
+English it's much better at responding
+
+01:05:13.000 --> 01:05:19.000
+uh to these queries than it is in any
+
+01:05:15.960 --> 01:05:21.039
+other language and in particular like
+
+01:05:19.000 --> 01:05:22.880
+lower resource languages or languages
+
+01:05:21.039 --> 01:05:26.400
+that are less similar to English it did
+
+01:05:22.880 --> 01:05:29.079
+much worse and notably we we counted the
+
+01:05:26.400 --> 01:05:32.160
+answer correct if it got it
+
+01:05:29.079 --> 01:05:34.279
+um we we had two settings one setting is
+
+01:05:32.160 --> 01:05:35.799
+we counted the answer correct if it only
+
+01:05:34.279 --> 01:05:38.359
+if it answered in the language we
+
+01:05:35.799 --> 01:05:39.680
+queried it in but we in other setting we
+
+01:05:38.359 --> 01:05:42.640
+also counted the answer correct if it
+
+01:05:39.680 --> 01:05:44.200
+answered in any language so we um it
+
+01:05:42.640 --> 01:05:46.640
+didn't necessarily have to even know the
+
+01:05:44.200 --> 01:05:48.200
+name of the entity in that uh language
+
+01:05:46.640 --> 01:05:50.520
+and we would still count it
+
+01:05:48.200 --> 01:05:54.720
+correct and so what I mean by there's a
+
+01:05:50.520 --> 01:05:56.440
+dichotomy between the information that
+
+01:05:54.720 --> 01:05:59.240
+language models have
+
+01:05:56.440 --> 01:06:02.480
+encoded and whether they're able to
+
+01:05:59.240 --> 01:06:02.480
+retrieve it
+
+01:06:02.680 --> 01:06:07.640
+is in English it's able to answer the
+
+01:06:06.000 --> 01:06:10.799
+models we tested were able to answer
+
+01:06:07.640 --> 01:06:13.000
+like 177% of queries
+
+01:06:10.799 --> 01:06:14.359
+but if the fact that they're able to
+
+01:06:13.000 --> 01:06:16.160
+answer in English means that the
+
+01:06:14.359 --> 01:06:18.520
+language model quote unquote knows the
+
+01:06:16.160 --> 01:06:20.200
+answer right like it knows the answer in
+
+01:06:18.520 --> 01:06:22.680
+English we're asking exactly the same
+
+01:06:20.200 --> 01:06:24.400
+question in all the other languages so
+
+01:06:22.680 --> 01:06:26.079
+you know it should know the answer in
+
+01:06:24.400 --> 01:06:27.680
+the other languages too
+
+01:06:26.079 --> 01:06:30.000
+but it's not able to retrieve the answer
+
+01:06:27.680 --> 01:06:33.079
+because we asked in another language
+
+01:06:30.000 --> 01:06:35.920
+so um that brings up some interesting
+
+01:06:33.079 --> 01:06:38.079
+questions about how we can make models
+
+01:06:35.920 --> 01:06:39.680
+better at retrieving the the knowledge
+
+01:06:38.079 --> 01:06:43.559
+that they already know in English when
+
+01:06:39.680 --> 01:06:45.520
+you query them in other languages or um
+
+01:06:43.559 --> 01:06:48.119
+and there was another paper recently I
+
+01:06:45.520 --> 01:06:52.720
+don't know if I'd be able to find it um
+
+01:06:48.119 --> 01:06:56.119
+exactly which is um they
+
+01:06:52.720 --> 01:07:01.799
+prompted models with personas and so
+
+01:06:56.119 --> 01:07:04.599
+they said I um you know I am a old man I
+
+01:07:01.799 --> 01:07:07.160
+am an old woman I am a young man I am
+
+01:07:04.599 --> 01:07:10.039
+young woman I am a child or something
+
+01:07:07.160 --> 01:07:12.799
+like that um or they also talked about
+
+01:07:10.039 --> 01:07:15.640
+things like uh physical disabilities and
+
+01:07:12.799 --> 01:07:17.200
+things and they said um please answer
+
+01:07:15.640 --> 01:07:19.640
+this question after they prompted with a
+
+01:07:17.200 --> 01:07:22.680
+Persona and just having that Persona
+
+01:07:19.640 --> 01:07:24.839
+greatly changed the ability of the model
+
+01:07:22.680 --> 01:07:26.400
+to answer questions so it's this very
+
+01:07:24.839 --> 01:07:28.200
+weird thing which which is like the
+
+01:07:26.400 --> 01:07:29.799
+models are actually capable of answering
+
+01:07:28.200 --> 01:07:31.520
+the questions but based on how you probe
+
+01:07:29.799 --> 01:07:32.880
+them whether it's in like different
+
+01:07:31.520 --> 01:07:34.599
+languages or if you give them a
+
+01:07:32.880 --> 01:07:36.839
+different Persona they manage to answer
+
+01:07:34.599 --> 01:07:39.000
+things differently and so on the plus
+
+01:07:36.839 --> 01:07:42.920
+side like you can create you can make
+
+01:07:39.000 --> 01:07:44.799
+ways to reduce the language models
+
+01:07:42.920 --> 01:07:45.920
+performance by giving it like a Persona
+
+01:07:44.799 --> 01:07:49.839
+that shouldn't be good at answering
+
+01:07:45.920 --> 01:07:53.279
+questions or something like that um
+
+01:07:49.839 --> 01:07:54.839
+but on the plus side um like when you're
+
+01:07:53.279 --> 01:07:57.279
+doing code generation there was this
+
+01:07:54.839 --> 01:07:58.960
+magic prompt which is like um I have
+
+01:07:57.279 --> 01:08:01.319
+checked this carefully in all the unit
+
+01:07:58.960 --> 01:08:03.240
+tests pass and that would improve your
+
+01:08:01.319 --> 01:08:05.760
+code generation accuracy by like five
+
+01:08:03.240 --> 01:08:07.559
+five points or something like that so um
+
+01:08:05.760 --> 01:08:09.240
+you just get the the model in the right
+
+01:08:07.559 --> 01:08:11.359
+mood to answer the question accurately
+
+01:08:09.240 --> 01:08:13.319
+and it does a better job at doing it so
+
+01:08:11.359 --> 01:08:15.960
+it's kind of uh it goes in both
+
+01:08:13.319 --> 01:08:15.960
+directions I
+
+01:08:16.679 --> 01:08:27.080
+guess cool um yeah uh any any questions
+
+01:08:23.679 --> 01:08:30.120
+here um another thing that you can do uh
+
+01:08:27.080 --> 01:08:31.000
+is fine-tune models specifically so
+
+01:08:30.120 --> 01:08:34.080
+they're good at answering
+
+01:08:31.000 --> 01:08:35.560
+knowledge-based questions so um uh this
+
+01:08:34.080 --> 01:08:38.080
+paper demonstrated that you could find
+
+01:08:35.560 --> 01:08:39.480
+tune models uh on synthetically created
+
+01:08:38.080 --> 01:08:41.159
+knowledge based questions and that would
+
+01:08:39.480 --> 01:08:42.920
+improve the ability of the model to
+
+01:08:41.159 --> 01:08:47.679
+answer questions about knowledge
+
+01:08:42.920 --> 01:08:47.679
+bases um it's
+
+01:08:49.120 --> 01:08:57.440
+uh yeah um it's pretty straightforward
+
+01:08:53.199 --> 01:08:57.440
+so uh there's that
+
+01:08:57.799 --> 01:09:03.120
+um yeah we already talked about this in
+
+01:09:00.000 --> 01:09:07.560
+the rag class so I think I might skip
+
+01:09:03.120 --> 01:09:10.239
+that um a final paper that I'd like to
+
+01:09:07.560 --> 01:09:12.600
+talk about this is also a paper uh done
+
+01:09:10.239 --> 01:09:13.759
+by my student Jung B Jong and this is
+
+01:09:12.600 --> 01:09:16.080
+interesting from the point of view of
+
+01:09:13.759 --> 01:09:18.000
+multihop reasoning and so I talked a
+
+01:09:16.080 --> 01:09:19.679
+little bit about like multihop reasoning
+
+01:09:18.000 --> 01:09:23.239
+along reasoning
+
+01:09:19.679 --> 01:09:26.159
+chains um in knowledge bases and this is
+
+01:09:23.239 --> 01:09:28.520
+one example of multihop reasoning
+
+01:09:26.159 --> 01:09:30.080
+among along reasoning chains within the
+
+01:09:28.520 --> 01:09:33.400
+parameters of the model so testing
+
+01:09:30.080 --> 01:09:36.759
+whether models can answer
+
+01:09:33.400 --> 01:09:38.480
+um Can it answer multihop questions and
+
+01:09:36.759 --> 01:09:40.839
+basically what we did here is we took a
+
+01:09:38.480 --> 01:09:42.679
+knowledge base and a knowledge base can
+
+01:09:40.839 --> 01:09:44.279
+have
+
+01:09:42.679 --> 01:09:49.480
+um
+
+01:09:44.279 --> 01:09:49.480
+like uh country country is
+
+01:09:49.600 --> 01:09:52.600
+US
+
+01:09:53.480 --> 01:09:58.600
+president um and then a
+
+01:10:00.880 --> 01:10:06.560
+birthday um and so we can create these
+
+01:10:04.280 --> 01:10:08.640
+multihop questions right uh and just
+
+01:10:06.560 --> 01:10:10.280
+follow the relation links and then we
+
+01:10:08.640 --> 01:10:11.440
+know the answer to the multihop question
+
+01:10:10.280 --> 01:10:13.560
+by following the link and we can
+
+01:10:11.440 --> 01:10:18.159
+generate you know the question given a
+
+01:10:13.560 --> 01:10:19.800
+template um so we did this and had like
+
+01:10:18.159 --> 01:10:22.800
+question one which is return the artist
+
+01:10:19.800 --> 01:10:25.719
+who recorded party a over um and then
+
+01:10:22.800 --> 01:10:28.159
+where in Georgia does uh Usher live and
+
+01:10:25.719 --> 01:10:29.920
+then we can turn this into a question
+
+01:10:28.159 --> 01:10:31.679
+which part of Georgia in which part of
+
+01:10:29.920 --> 01:10:34.239
+Georgia does the artist that recorded
+
+01:10:31.679 --> 01:10:37.560
+the party8 overlive and so we now have a
+
+01:10:34.239 --> 01:10:45.000
+multi multihop question and what we did
+
+01:10:37.560 --> 01:10:47.440
+is we measured whether um the model was
+
+01:10:45.000 --> 01:10:49.760
+able to answer the first question the
+
+01:10:47.440 --> 01:10:53.320
+second question and the comp like
+
+01:10:49.760 --> 01:10:56.120
+compound question and what we found is
+
+01:10:53.320 --> 01:10:59.440
+like what we would expect
+
+01:10:56.120 --> 01:11:01.719
+if models were like perfect knowledge
+
+01:10:59.440 --> 01:11:04.360
+processors right
+
+01:11:01.719 --> 01:11:08.120
+is we have
+
+01:11:04.360 --> 01:11:10.800
+like yes on the first question
+
+01:11:08.120 --> 01:11:14.000
+no
+
+01:11:10.800 --> 01:11:16.560
+yes um yes on the first question and no
+
+01:11:14.000 --> 01:11:16.560
+on the first
+
+01:11:17.199 --> 01:11:24.760
+question and we would expect that
+
+01:11:21.920 --> 01:11:26.080
+basically if it knew both of the answers
+
+01:11:24.760 --> 01:11:27.239
+to the first question and the second
+
+01:11:26.080 --> 01:11:30.600
+question it would get the compound
+
+01:11:27.239 --> 01:11:31.800
+question right and if it got uh like
+
+01:11:30.600 --> 01:11:34.800
+either of them wrong it would get it
+
+01:11:31.800 --> 01:11:37.120
+wrong right um you know in the in the
+
+01:11:34.800 --> 01:11:39.400
+ideal world where the knowledge of the
+
+01:11:37.120 --> 01:11:41.280
+two sub questions is necessary to answer
+
+01:11:39.400 --> 01:11:43.880
+the comp composite question and the
+
+01:11:41.280 --> 01:11:45.840
+model is a perfect knowledge processor
+
+01:11:43.880 --> 01:11:47.120
+and basically what we found we tried a
+
+01:11:45.840 --> 01:11:49.280
+whole bunch of different types of
+
+01:11:47.120 --> 01:11:51.199
+questions and what we found is this is
+
+01:11:49.280 --> 01:11:55.960
+totally not the case like it's not the
+
+01:11:51.199 --> 01:11:58.520
+case at all um and what we found in said
+
+01:11:55.960 --> 01:12:01.560
+is if it's able to answer the second
+
+01:11:58.520 --> 01:12:04.120
+question correctly it was much more
+
+01:12:01.560 --> 01:12:07.480
+likely to be able to answer the
+
+01:12:04.120 --> 01:12:08.840
+composite question um even if it can
+
+01:12:07.480 --> 01:12:11.000
+answer the first question that has
+
+01:12:08.840 --> 01:12:13.120
+almost no relation with whether it could
+
+01:12:11.000 --> 01:12:15.520
+answer the composite question at all so
+
+01:12:13.120 --> 01:12:17.679
+it's more like somehow from the answer
+
+01:12:15.520 --> 01:12:19.320
+to the second question it was able to to
+
+01:12:17.679 --> 01:12:22.280
+get the answer right and it kind of
+
+01:12:19.320 --> 01:12:24.040
+makes sense actually because like um
+
+01:12:22.280 --> 01:12:26.320
+let's say the answer to the second
+
+01:12:24.040 --> 01:12:27.920
+question is some like really long list
+
+01:12:26.320 --> 01:12:30.719
+like who are all the presidents of the
+
+01:12:27.920 --> 01:12:33.320
+United States um or something like that
+
+01:12:30.719 --> 01:12:35.639
+that's just hard to answer um so if I
+
+01:12:33.320 --> 01:12:38.000
+said who are all the presidents of the
+
+01:12:35.639 --> 01:12:40.800
+country where Washington DC is located
+
+01:12:38.000 --> 01:12:42.679
+in um you know like the second question
+
+01:12:40.800 --> 01:12:44.040
+is really hard so that's hard to get but
+
+01:12:42.679 --> 01:12:46.120
+if I say
+
+01:12:44.040 --> 01:12:49.920
+um
+
+01:12:46.120 --> 01:12:53.520
+uh what what is the
+
+01:12:49.920 --> 01:12:57.120
+capital what is the capital of the
+
+01:12:53.520 --> 01:12:57.120
+country uh
+
+01:12:57.400 --> 01:13:02.440
+what is what is the capital of the
+
+01:12:58.840 --> 01:13:05.400
+country where the most
+
+01:13:02.440 --> 01:13:06.800
+um people live or something like that
+
+01:13:05.400 --> 01:13:08.679
+even if you weren't sure about the
+
+01:13:06.800 --> 01:13:10.880
+country where the most people live you
+
+01:13:08.679 --> 01:13:13.040
+could pick a random capital and get it
+
+01:13:10.880 --> 01:13:16.199
+right some of the time or something like
+
+01:13:13.040 --> 01:13:18.239
+that so um that's what we found in this
+
+01:13:16.199 --> 01:13:19.800
+paper and I I think like another nice
+
+01:13:18.239 --> 01:13:22.360
+thing about knowledge bases is they
+
+01:13:19.800 --> 01:13:24.880
+allow you to ask like really interesting
+
+01:13:22.360 --> 01:13:26.400
+questions like this about what language
+
+01:13:24.880 --> 01:13:29.120
+model know or what language models don't
+
+01:13:26.400 --> 01:13:31.040
+know in a structured way so um I think
+
+01:13:29.120 --> 01:13:32.280
+if you're interested in probing language
+
+01:13:31.040 --> 01:13:35.320
+models and what they know and what they
+
+01:13:32.280 --> 01:13:38.639
+can infer what logic they can do that's
+
+01:13:35.320 --> 01:13:42.320
+good um cool yeah that's all I have for
+
+01:13:38.639 --> 01:13:44.920
+today um are there any questions or
+
+01:13:42.320 --> 01:13:48.679
+discussion or things like that or happy
+
+01:13:44.920 --> 01:13:48.679
+to talk up here too