19 ms·

Recent Advances in Natural Language Processing

278 points by saadalem 6y ago

p1esk 6y ago
Note this is pre-GPT-3. In fact I expect GPT-4 will be where interesting things start happening in NLP.
- curiousgal 6y ago
  I honestly don't get where the big deal is with NLP. So far the most useful application has been customer support chatbots and those still don't rise to the level of having an actual human that can understand the intricacies of your special request.
  - p1esk 6y ago
    those still don't rise to the level of having an actual human What if they did? Do you see where the big deal is now?
  - thekyle 6y ago
    If a chatbot was as good as a human then would you notice it was a chatbot?
  - feral 6y ago
    I work on such a bot @Intercom. A lot of support requests aren't actually intricate and special: there's almost always loads of simple requests that come in again and again. When you ask a request like that and get the answer instantly, that really is ML delivering value. You mightn't think it, but a lot of people work in customer support, and spend a lot of time answering rote questions again and again. People talk about how much hype there is, or the chance of another AI winter. Yes there's a ton of hype - but I think they aren't considering the real value already being delivered this time around. Everyone is excited about GPT3, but there's already been amazing progress in practical NLP already, over the last few years.
  - ben_w 6y ago
    Current NLP is bad. Still useful (Google search increasingly feels like it is doing NLP to change what I asked for into what it thinks I meant) but bad. A hypothetical future “perfect” NLP can demonstrate any skill that a human could learn by reading, and computers can read so much more than any given human.
    - plafl 6y ago
      Is reading enough to understand the real world without direct experience of the real world? Is there any research that tries to answer this question?
      - rvense 6y ago
        As of about ten years ago when I received my degree in linguistics, I understood there were two schools on this issue: A: Of course not, let's do something else. B: What? You put text in the maths and stuff comes out.
      - p1esk 6y ago
        Is there any research that tries to answer this question? That's the whole point of the experiment called GPT-3.
  - MiroF 6y ago
    That's just not even close to the only or most useful application. I use NLP and associates s2s techniques every day. I struggle to see how so many people don't see the obvious benefits deep inference is bringing to stuff all around them.
    - sprobertson 6y ago
      Could be a more convincing statement with examples...
      - MiroF 6y ago
        Speech recognition on any device? Translation?
  - claudeganon 6y ago
    This past week, I used this new edit distance library to identify quasi-duplicates in a large dataset: https://github.com/Bergvca/string_grouper https://github.com/Bergvca/string_grouper Saved me hours of work because all the Levenshtein implementations are pretty slow and I’m going to need to rerun the analysis as the dataset grows. I don’t know about consumer-facing tools, but NLP stuff has helped me solve all kinds of tedious data problems at work.
narag 6y ago
Has it happened that a "thought experiment" has become a real experiment ever?
- dane-pgp 6y ago
  Most historians think that this was actually a thought experiment: https://en.wikipedia.org/wiki/Galileo's_Leaning_Tower_of_Pisa_experiment https://en.wikipedia.org/wiki/Galileo's_Leaning_Tower_of_Pis... An equivalent experiment was famously carried out for real in 1971 on the surface of the Moon.
- simonh 6y ago
  In a sense every experiment starts as a thought experiment.
- exo-pla-net 6y ago
  Not sure exactly what you mean, but Einstein developed relativity largely through thought experiments. And relativity has been verified by real experiments.
jvanderbot 6y ago
I'd go one step further: Humans themselves don't understand anything, we are just good at constructing logical-sounding (plausible, testable) stories about things. These are mental models, and it's the only way we can make reasonable predictions to within error tolerances of our day-to-day experience, but they are flat-out lies and stories we tell ourselves not based on a high-fidelity understanding of anything. Rumination, deep thinking, etc is simply actor-critic learning of these mental models for story-telling.
- soulofmischief 6y ago
  Mental models are not lies. "The car is red" is not a lie just because I didn't phrase it internally as "The car reflects photons of a frequency of around 700nm". We have to be able to simplify and internalize simplified models in order to make any sense of anything. It's the same reason your eyes only have a focal point in the dead center: attention requires vast amounts of processing power. To reiterate, a simplification is not a lie. Especially not a flat-out lie.
  - Turing_Machine 6y ago
    Yes. Bridges are designed using old-school Newtonian mechanics, without (in general) considering relativistic or quantum effects at all, but they stand up nonetheless. That wouldn't be the case if they were built based on "flat-out lies". Edit: the sticking point here is the use of the word "lie", I think. A lie is a falsehood told with intent to deceive. Thus, novels are not "lies", despite being works of the imagination. Everyone knows that the people in Moby-Dick or Wuthering Heights were made up. Neither is a simplified model a "lie" if it is close enough. All engineers know that the steel and concrete components of a bridge have quantum and relativistic effects going on inside them, so ignoring those effects isn't a "lie" in any meaningful sense. It just doesn't matter for the purpose at hand.
    - vladTheInhaler 6y ago
      This is sort of a digression, sort of a return to the original topic. As I was reading the sentence about Moby-Dick, I wondered if perhaps someday this comment section would become part of a training corpus for GPT-9001 or whatever. Then a researcher might read it a paragraph from Moby-Dick and ask it whether those events actually happened or whether they were fictional. And it might answer "Of course they were made up. Everyone knows that the people in Moby-Dick or Wuthering Heights were made up." And if the researcher asked it to explain how it could know anything about what is fiction and what is not, it might be able to use the contents of these comments to figure out how to converse intelligently about its own limitations.
mqus 6y ago
Not a single mention if this is only applicable to english or to other natural languages. Afaict this mostly lists advancements in ELP (english language processing), Especially the Winograd schema (or ar least the given example) seems to be heavily focused on english. Relevant article for this problem: https://news.ycombinator.com/item?id=24026511 https://news.ycombinator.com/item?id=24026511
- MiroF 6y ago
  But there's no reason the models are english specific...
  - woodson 6y ago
    In a certain way, they could be, simply because their structure may work better on the type of language that English is. It may not work as well for languages that exhibit other grammatical patterns (e.g. morphologically rich languages, or those that exhibit superficially more flexible word order which nonetheless conveys information about topic/focus).
    - dvduval 6y ago
      Some languages like Chinese have a large corpus of information that can be studied, and there has been a continuous effort to standardize the language. it wouldn't surprise me if it turns out to be easier than English. I would expect it would be more difficult when you're talking about languages that have regional differences in usage of words, perhaps northern Vietnamese vs. Southern Vietnamese, or the Spanish of Spain versus the Spanish of Mexico.
      - east2west 6y ago
        Chinese has large regional differences, too. Cantonese has different grammar, not just word usage. Modern Chinese grammar is somewhat similar to English while Cantonese follows ancient Chinese, which places verb to the end of sentence.
bloaf 6y ago
I know that there are allegedly NLP algorithms for generating things like articles about sports games. I assume they have something more like the type signature (timeline of events) -> (narrative about said events) What this article is about is more (question/prompt) -> (answer/continuation of prompt) Does anyone know if there is progress in the (timeline of events) -> (narrative about said events) space?
- 082349872349872 6y ago
  For an intermediate goal on the way to sports games, the financial press version of (timeline of events) -> (narrative about said events) could be tackled as a memoryless system.
YeGoblynQueenne 6y ago
>> The Winograd schema test was originally intended to be a more rigorous replacement for the Turing test, because it seems to require deep knowledge of how things fit together in the world, and the ability to reason about that knowledge in a linguistic context. Recent advances in NLP have allowed computers to achieve near human scores:(https://gluebenchmark.com/leaderboard/ https://gluebenchmark.com/leaderboard/). The "Winograd schema" in Glue/SuperGlue refers to the Winograd-NLI benchmark which is simplified with respect to the original Winograd Schema Challenge [1], on which the state-of-the-art still significantly lags human performance: The Winograd Schema Challenge is a dataset for common sense reasoning. It employs Winograd Schema questions that require the resolution of anaphora: the system must identify the antecedent of an ambiguous pronoun in a statement. Models are evaluated based on accuracy. WNLI is a relaxation of the Winograd Schema Challenge proposed as part of the GLUE benchmark and a conversion to the natural language inference (NLI) format. The task is to predict if the sentence with the pronoun substituted is entailed by the original sentence. While the training set is balanced between two classes (entailment and not entailment), the test set is imbalanced between them (35% entailment, 65% not entailment). The majority baseline is thus 65%, while for the Winograd Schema Challenge it is 50% (Liu et al., 2017). The latter is more challenging. https://nlpprogress.com/english/common_sense.html https://nlpprogress.com/english/common_sense.html There is also a more recent adversarial version of the Winograd Schema Challenge called Winogrande. I can't say I'm on top of the various results and so I don't know the state of the art, but it's not yet "near human", not without caveats (for example, wikipedia reports 70% accuracy on 70 problems manually selected from the originoal WSC). __________ [1] https://www.aaai.org/ocs/index.php/KR/KR12/paper/view/4492 https://www.aaai.org/ocs/index.php/KR/KR12/paper/view/4492
YeGoblynQueenne 6y ago
Separate comment because separate point. To my mind, all this benchmark-beating is just pointless faffing about and not a little frustrating to see that this is where the current state of the art is at. NLP systems keep getting better and better at beating benchmarks and yet we still don't have artificial agents that can hold an intelligent conversation, even in a restricted context. Or rather, we had such programs since the late 1960's, with no one else's than Terry Winograd's own doctoral thesis system, SHRDLU [1], but it seems that in the race to build powerful classifiers, we forgot how to build systems that can interact intelligently with a human. Here's a small excerpt from SHRDLU (taken from the wikipedia page on SHRDLU, originally in an appendix in Terry Winograd's thesis): Person: Is there a large block behind a pyramid? Computer: YES, THREE OF THEM: A LARGE RED ONE, A LARGE GREEN CUBE, AND THE BLUE ONE. Person: Put a small one onto the green cube which supports a pyramid. Computer: OK. Person: Put the littlest pyramid on top of it. Computer: OK. Person: Does the shortest thing the tallest pyramid's support supports support anything green? Computer: YES, THE GREEN PYRAMID. Person: What color is it? Computer: BY "IT", I ASSUME YOU MEAN THE SHORTEST THING THE TALLEST PYRAMID'S SUPPORT SUPPORTS. Computer: RED Note that this is not just mindless text generation. The conversation is held in the context of a "blocks world" where a robot with a single hand and eye ("Computer" in the excerpt above) moves blocks of various shapes and colours around, as directed by a human user in free-form natural language. When the Computer says "OK" after it's directed to "put the littlest pyramid on top of it" it's because it really has grabbed the smallest pyramid in the blocks world and placed it on top of the small block in an earlier sentence, as the Person asked. The program has a memory module to keep track of what ellipses like "it", "one" etc refer to throughout the conversation. SHRDLU was a traditional program hand-crafted by a single PhD student- no machine learning, no statistical techniques. It included, among other things, a context-free grammar (!) of natural English and a planner (to control the robot's hand) all written in Lisp and PLANNER. In its limited domain, it was smarter than anything ever created with statisical NLP methods. ______________________ [1] https://en.wikipedia.org/wiki/SHRDLU https://en.wikipedia.org/wiki/SHRDLU
- quotemstr 6y ago
  Why is it surprising that a CFG can approximate a subset of English grammar?
ascavalcante80 6y ago
NLP is great for many things, but, from my own experience as a NLP developer, machines are not even close to understand human language. They can interpret well some kind of written speech, but they will struggle to grasp two humans speaking to each other. The progress we are make on building chatbots and vocal assistants is mainly due to the fact We are learning how to speaking to the machines, and not the contrary.
plutonorm 6y ago
I think the standard responses you see about gpt-3 - Hur durr, can't have intelligence from statistics. Hurt dur, Chinese room. Hur durr, doesn't understand semantics, it's just dumb. Ect ect ect ad nausea. They, go to show not how dumb gpt-3 is but how unthinking most people are. Just pulling from a bag of generally agreed upon notions, matching them up in simplistic ways and regurgitating.
FiberBundle 6y ago
I found the science exams results interesting and skimmed the paper [1]. They report an accuracy of >90% on the questions. What I found puzzling was that they have a section in the experimental results part where they test the robustness of the results using adverserial answer options, more specifically they used some simple heuristic to choose 4 additional answer options from the set of other questions which maximized 'confusion' for the model. This resulted in a drop of more than 40 percentage points in the accuracy of the model. I find this extremely puzzling, what do these models actually learn? Clearly they don't actually learn any scientific principles. [1] https://arxiv.org/pdf/1909.01958.pdf https://arxiv.org/pdf/1909.01958.pdf
- wrs 6y ago
  I would be interested in hearing the results from humans presented with adversarial answer options. You may say that a machine learning correlations between words isn’t really learning science, but I wonder how many human students aren’t either, just pretty much learning correlations between words to pass tests...
  - FiberBundle 6y ago
    They do give an example of a question, in which the model chose an incorrect answer in the adversarial setting: "The condition of the air outdoors at a certain time ofday is known as (A) friction (B) light (C) force (D)weather[correct](Q) joule (R) gradient[selected](S)trench (T) add heat" I assume this might be characteristic for other questions as well, although I don't know anything about the Regents Science Exam and whether there are multiple questions about closely related topics.
    - taneq 6y ago
      That’s a terribly worded question anyway. Of the original answers, ‘weather’ is the least worst but it’s still vague.
      - mannykannot 6y ago
rvense 6y ago
I think the point about language being a model of reality was interesting. I have an MA in linguistics including some NLP from about a decade ago and was looking at a career in academic NLP. I ultimately left to become a programmer because (of life circumstances and the fact that) I didn't see much of a future for the field, precisely because it was ignoring the (to me) obvious issues of written language bias, ignorance of multi-modality and situatedness etc. that are brought up in this post. All of these results are very interesting, but I'm not really feeling like we've been proved wrong yet. There is a big question of scalability here, at least as far as the goal of AGI goes, which the author also admits: > Of course everyday language stands in a woolier relation to sheep, pine cones, desire and quarks than the formal language of chess moves stands in relation to chess moves, and the patterns are far more complex. Modality, uncertainty, vagueness and other complexities enter but the isomorphism between world and language is there, even if inexact. This woolly relation between language and reality is well-known. It has been studied in various ways in linguistics and the philosophy of language, for instance by Frege and not least Foucault and everything after. I also think many modern linguistic schools take a very different view of "uncertainty and vagueness" than I sense in the author here, but they are obviously writing for non-specialist audience and trying not to dwell on this subject here. My point is, when making and evaluating these NLP methods and the tools they are used to construct, it is extremely important to understand that language models social realities rather than any single physical one. It seems to me all too easy, coming from formal grammar or pure stats or computer science, to rush into these things with naive assumptions about what words are or how they mean things to people. I dread to think what will happen if we base our future society on tools made in that way.
- skybrian 6y ago
  These tools seem to be getting pretty good at fiction. In particular, playing around with AI Dungeon, it doesn't believe anything, or alternately you could say it believes everything at once. It's similar to a library that contains books by different authors, some fact, some fiction. Contradictions don't matter. Only local consistency matters, and even then, not that much. Unfortunately, many people want to believe that they are being understood. But, on the bright side, this stuff seems artistically useful? Entertainment is a big business.
skybrian 6y ago
Darn, based on the title, I was hoping for an overview of recent research. Lots of people are having fun playing with GPT-3 or AI Dungeon, myself included, but it seems like there is other interesting research going on like the REALM paper [1], [2]. What should I be reading? Why aren't people talking about REALM more? I'm no expert, but it seems like keeping the knowledge base outside the language model has a lot going for it? [1] https://ai.googleblog.com/2020/08/realm-integrating-retrieval-into.html https://ai.googleblog.com/2020/08/realm-integrating-retrieva... [2] https://arxiv.org/abs/2002.08909 https://arxiv.org/abs/2002.08909
- nl 6y ago
  REALM is amazing, and Google's PEGASUS[1] taught itself to count between 2 and 5 (which is mindblowing). Basically the NLP groups from Google and FB[2][3] are always worth watching. [1] https://ai.googleblog.com/2020/06/pegasus-state-of-art-model-for.html https://ai.googleblog.com/2020/06/pegasus-state-of-art-model... [2] https://ai.facebook.com/blog/covost-v2-expanding-the-largest-most-diverse-multilingual-speech-to-text-translation-data-set/ https://ai.facebook.com/blog/covost-v2-expanding-the-largest... [3] https://ai.facebook.com/blog/introducing-a-new-large-scale-dynamic-data-set-to-push-the-limits-of-natural-language-processing/ https://ai.facebook.com/blog/introducing-a-new-large-scale-d...
- MiroF 6y ago
  I think there's a pretty large gap between what people in academia NLP/industry labs find interesting and what the bitcoing/VC/whatever people on twitter seem to find interesting. realm is definitely cool, and i agree that techniques like this are the way forward.
_emacsomancer_ 6y ago
A bit I found rather strange, on the language-side: > This is to say the patterns in language use mirror the patterns of how things are(1). > (1)- Strictly of course only the patterns in true sentences mirror, or are isomorphic to, the arrangement of the world, but most sentences people utter are at least approximately true. Presumably this should really say something like "...but most sentences people utter are at least approximately true of their mental representation of the world."
rllin 6y ago
the thing is humans have most efficiently encoded (in detail) reality in text. humans already highlight what is worth encoding about reality. for example, you can finetune gpt-2 to have an idea of sexual biology by having it read erotica. just like how you can have a model learn the same by watching porn. but it is much more efficient to read the text, since there is much less information that is "useless"
rland 6y ago
> Models are transitive- if x models y, and y models z, then x models z. The upshot of these facts are that if you have a really good statistical model of how words relate to each other, that model is also implicitly a model of the world. This right here is a great way of putting the success of GPT-3 into context. We think GPT is smart, because when it says something eerily human-like we apply our model of the world onto what it is saying. A conversation like this: > Me: So, what happened when you fell off the balance beam? > GPT: It hurt. > Me: Why'd it hurt so bad? > GPT: The beam was high up and I feel awkwardly. > Me: Wow, that sounds awful. In this conversation, one of us is thinking far harder than the other. GPT can have conversations like this now, which is impressive. But only I can model the beam, the fall, and the physical reality. When I say "that sounds awful," I actually do a miniature physics simulation in my head, imagining losing my balance and falling off a high beam, landing, the physical pain, etc. GPT does none of that. In either case, when it asks the question or when it answers it, it is entirely ignorant of this sort of "shadow" model that's being constructed. Generalizing a bit, our "shadow" model of reality in every single domain is far more powerful than language's approximation. That's why we won't be able to use GPT to do a medical diagnosis or create a piece of architecture or whatever else people are saying it's going to do now.
- ilaksh 6y ago
  Right. I recently saw a podcast with Dileep George of Vicarious AI. I don't know if his specific techniques will work fully generally but at the high level he is talking about something quite similar to what you are saying in terms of grounding language understanding and having real models of the world. So I am definitely following him. More broadly there is a growing group of researchers who are working on trying to achieve better world modeling (although of course there have been many for decades, it just seems that the number who are coming from deep learning towards AGI with more emphasis on world modeling is increasing). Such as Ferran Alet. Sort of the Tenenbaum school I guess. Or Melanie Mitchell has been saying similar for quite awhile. They have recent talks on Good AI's YouTube channel.
- dwohnitmok 6y ago
  The author's quote is asserting the opposite of what you're saying here. Filling in "GPT" for x, "text" for y, and "world" for z, the author is stating "if GPT models text, and text models the world, then GPT models the world." In particular, the author directly addresses your point that "Generalizing a bit, our 'shadow' model of reality in every single domain is far more powerful than language's approximation." > Modality, uncertainty, vagueness and other complexities enter but the isomorphism between world and language is there, even if inexact. My own personal view tends to lean towards the author's. GPT-3 , despite its many astonishing achievements, has many many limitations. Yet I don't see any indication as of yet that any of those limitations are fundamental ones inherent to the approach behind the GPT series, which is why GPT-3 both excites and frightens me.
ragebol 6y ago
> In my head- and maybe this was naive- I had thought that, in order to attempt these sorts of tasks with any facility, it wouldn’t be sufficient to simply feed a computer lots of text. (Tasks here referring to questions in the New York Regent’s science exam) Same for me. But it makes sense of course that learning from text only is entirely possible. I certainly have not directly observed the answer to eg. 'Which process in an apple tree primarily results from cell division? (1) growth (2) photosynthesis (3) gas exchange (4) waste removal', I have been taught, from text books, what the answer should be. I do have a much better grounding of what growth is, what apples and apple trees are though.
walleeee 6y ago
> A lot of the power of the thought experiment hinges on the fact that the room solves questions using a lookup table, this stacks the deck. Perhaps we be more willing to say that the room as a whole understood language if it formed an (implicit) model of how things are, and of the current context, and used those models to answer questions. Some define intelligence (entirely separately from consciousness) precisely as the ability to develop an internal model. Coupled to a regulatory feedback the system can then modify itself in response to some set of internal and/or external conditions (Joscha Bach for instance suggests consciousness is a consequence of extremely complex self-models)
laurieg 6y ago
I find it a little bit strange that there is an unspoked assumption in almost all natural language processing: That speech and text are perfectly equivalent. All of the examples in the article work on English text, not spoken English. I would consider spoken English to be a much better "Gold standard" of natural language. I'm really looking forward to machine translation operating purely on a speech in/speech out basis, instead of converting to text as an intermediate step.
benibela 6y ago
Rather than a generator, I could use a good verifier, i.e., an accurate grammar checker

278 points by saadalem 6y ago

p1esk 6y ago

Note this is pre-GPT-3. In fact I expect GPT-4 will be where interesting things start happening in NLP.

curiousgal 6y ago
I honestly don't get where the big deal is with NLP. So far the most useful application has been customer support chatbots and those still don't rise to the level of having an actual human that can understand the intricacies of your special request.
- p1esk 6y ago
  those still don't rise to the level of having an actual human What if they did? Do you see where the big deal is now?
- thekyle 6y ago
  If a chatbot was as good as a human then would you notice it was a chatbot?
- feral 6y ago
  I work on such a bot @Intercom. A lot of support requests aren't actually intricate and special: there's almost always loads of simple requests that come in again and again. When you ask a request like that and get the answer instantly, that really is ML delivering value. You mightn't think it, but a lot of people work in customer support, and spend a lot of time answering rote questions again and again. People talk about how much hype there is, or the chance of another AI winter. Yes there's a ton of hype - but I think they aren't considering the real value already being delivered this time around. Everyone is excited about GPT3, but there's already been amazing progress in practical NLP already, over the last few years.
- ben_w 6y ago
  Current NLP is bad. Still useful (Google search increasingly feels like it is doing NLP to change what I asked for into what it thinks I meant) but bad. A hypothetical future “perfect” NLP can demonstrate any skill that a human could learn by reading, and computers can read so much more than any given human.
  - plafl 6y ago
    Is reading enough to understand the real world without direct experience of the real world? Is there any research that tries to answer this question?
    - rvense 6y ago
      As of about ten years ago when I received my degree in linguistics, I understood there were two schools on this issue: A: Of course not, let's do something else. B: What? You put text in the maths and stuff comes out.
    - p1esk 6y ago
      Is there any research that tries to answer this question? That's the whole point of the experiment called GPT-3.
- MiroF 6y ago
  That's just not even close to the only or most useful application. I use NLP and associates s2s techniques every day. I struggle to see how so many people don't see the obvious benefits deep inference is bringing to stuff all around them.
  - sprobertson 6y ago
    Could be a more convincing statement with examples...
    - MiroF 6y ago
      Speech recognition on any device? Translation?
- claudeganon 6y ago
  This past week, I used this new edit distance library to identify quasi-duplicates in a large dataset: https://github.com/Bergvca/string_grouper https://github.com/Bergvca/string_grouper Saved me hours of work because all the Levenshtein implementations are pretty slow and I’m going to need to rerun the analysis as the dataset grows. I don’t know about consumer-facing tools, but NLP stuff has helped me solve all kinds of tedious data problems at work.

narag 6y ago

Has it happened that a "thought experiment" has become a real experiment ever?

dane-pgp 6y ago
Most historians think that this was actually a thought experiment: https://en.wikipedia.org/wiki/Galileo's_Leaning_Tower_of_Pisa_experiment https://en.wikipedia.org/wiki/Galileo's_Leaning_Tower_of_Pis... An equivalent experiment was famously carried out for real in 1971 on the surface of the Moon.
simonh 6y ago
In a sense every experiment starts as a thought experiment.
exo-pla-net 6y ago
Not sure exactly what you mean, but Einstein developed relativity largely through thought experiments. And relativity has been verified by real experiments.

jvanderbot 6y ago

I'd go one step further: Humans themselves don't understand anything, we are just good at constructing logical-sounding (plausible, testable) stories about things. These are mental models, and it's the only way we can make reasonable predictions to within error tolerances of our day-to-day experience, but they are flat-out lies and stories we tell ourselves not based on a high-fidelity understanding of anything. Rumination, deep thinking, etc is simply actor-critic learning of these mental models for story-telling.

soulofmischief 6y ago
Mental models are not lies. "The car is red" is not a lie just because I didn't phrase it internally as "The car reflects photons of a frequency of around 700nm". We have to be able to simplify and internalize simplified models in order to make any sense of anything. It's the same reason your eyes only have a focal point in the dead center: attention requires vast amounts of processing power. To reiterate, a simplification is not a lie. Especially not a flat-out lie.
- Turing_Machine 6y ago
  Yes. Bridges are designed using old-school Newtonian mechanics, without (in general) considering relativistic or quantum effects at all, but they stand up nonetheless. That wouldn't be the case if they were built based on "flat-out lies". Edit: the sticking point here is the use of the word "lie", I think. A lie is a falsehood told with intent to deceive. Thus, novels are not "lies", despite being works of the imagination. Everyone knows that the people in Moby-Dick or Wuthering Heights were made up. Neither is a simplified model a "lie" if it is close enough. All engineers know that the steel and concrete components of a bridge have quantum and relativistic effects going on inside them, so ignoring those effects isn't a "lie" in any meaningful sense. It just doesn't matter for the purpose at hand.
  - vladTheInhaler 6y ago
    This is sort of a digression, sort of a return to the original topic. As I was reading the sentence about Moby-Dick, I wondered if perhaps someday this comment section would become part of a training corpus for GPT-9001 or whatever. Then a researcher might read it a paragraph from Moby-Dick and ask it whether those events actually happened or whether they were fictional. And it might answer "Of course they were made up. Everyone knows that the people in Moby-Dick or Wuthering Heights were made up." And if the researcher asked it to explain how it could know anything about what is fiction and what is not, it might be able to use the contents of these comments to figure out how to converse intelligently about its own limitations.

mqus 6y ago

Not a single mention if this is only applicable to english or to other natural languages. Afaict this mostly lists advancements in ELP (english language processing), Especially the Winograd schema (or ar least the given example) seems to be heavily focused on english. Relevant article for this problem: https://news.ycombinator.com/item?id=24026511 https://news.ycombinator.com/item?id=24026511

MiroF 6y ago
But there's no reason the models are english specific...
- woodson 6y ago
  In a certain way, they could be, simply because their structure may work better on the type of language that English is. It may not work as well for languages that exhibit other grammatical patterns (e.g. morphologically rich languages, or those that exhibit superficially more flexible word order which nonetheless conveys information about topic/focus).
  - dvduval 6y ago
    Some languages like Chinese have a large corpus of information that can be studied, and there has been a continuous effort to standardize the language. it wouldn't surprise me if it turns out to be easier than English. I would expect it would be more difficult when you're talking about languages that have regional differences in usage of words, perhaps northern Vietnamese vs. Southern Vietnamese, or the Spanish of Spain versus the Spanish of Mexico.
    - east2west 6y ago
      Chinese has large regional differences, too. Cantonese has different grammar, not just word usage. Modern Chinese grammar is somewhat similar to English while Cantonese follows ancient Chinese, which places verb to the end of sentence.

bloaf 6y ago

I know that there are allegedly NLP algorithms for generating things like articles about sports games. I assume they have something more like the type signature (timeline of events) -> (narrative about said events) What this article is about is more (question/prompt) -> (answer/continuation of prompt) Does anyone know if there is progress in the (timeline of events) -> (narrative about said events) space?

082349872349872 6y ago
For an intermediate goal on the way to sports games, the financial press version of (timeline of events) -> (narrative about said events) could be tackled as a memoryless system.

YeGoblynQueenne 6y ago

>> The Winograd schema test was originally intended to be a more rigorous replacement for the Turing test, because it seems to require deep knowledge of how things fit together in the world, and the ability to reason about that knowledge in a linguistic context. Recent advances in NLP have allowed computers to achieve near human scores:(https://gluebenchmark.com/leaderboard/ https://gluebenchmark.com/leaderboard/). The "Winograd schema" in Glue/SuperGlue refers to the Winograd-NLI benchmark which is simplified with respect to the original Winograd Schema Challenge [1], on which the state-of-the-art still significantly lags human performance: The Winograd Schema Challenge is a dataset for common sense reasoning. It employs Winograd Schema questions that require the resolution of anaphora: the system must identify the antecedent of an ambiguous pronoun in a statement. Models are evaluated based on accuracy. WNLI is a relaxation of the Winograd Schema Challenge proposed as part of the GLUE benchmark and a conversion to the natural language inference (NLI) format. The task is to predict if the sentence with the pronoun substituted is entailed by the original sentence. While the training set is balanced between two classes (entailment and not entailment), the test set is imbalanced between them (35% entailment, 65% not entailment). The majority baseline is thus 65%, while for the Winograd Schema Challenge it is 50% (Liu et al., 2017). The latter is more challenging. https://nlpprogress.com/english/common_sense.html https://nlpprogress.com/english/common_sense.html There is also a more recent adversarial version of the Winograd Schema Challenge called Winogrande. I can't say I'm on top of the various results and so I don't know the state of the art, but it's not yet "near human", not without caveats (for example, wikipedia reports 70% accuracy on 70 problems manually selected from the originoal WSC). __________ [1] https://www.aaai.org/ocs/index.php/KR/KR12/paper/view/4492 https://www.aaai.org/ocs/index.php/KR/KR12/paper/view/4492

YeGoblynQueenne 6y ago

Separate comment because separate point. To my mind, all this benchmark-beating is just pointless faffing about and not a little frustrating to see that this is where the current state of the art is at. NLP systems keep getting better and better at beating benchmarks and yet we still don't have artificial agents that can hold an intelligent conversation, even in a restricted context. Or rather, we had such programs since the late 1960's, with no one else's than Terry Winograd's own doctoral thesis system, SHRDLU [1], but it seems that in the race to build powerful classifiers, we forgot how to build systems that can interact intelligently with a human. Here's a small excerpt from SHRDLU (taken from the wikipedia page on SHRDLU, originally in an appendix in Terry Winograd's thesis): Person: Is there a large block behind a pyramid? Computer: YES, THREE OF THEM: A LARGE RED ONE, A LARGE GREEN CUBE, AND THE BLUE ONE. Person: Put a small one onto the green cube which supports a pyramid. Computer: OK. Person: Put the littlest pyramid on top of it. Computer: OK. Person: Does the shortest thing the tallest pyramid's support supports support anything green? Computer: YES, THE GREEN PYRAMID. Person: What color is it? Computer: BY "IT", I ASSUME YOU MEAN THE SHORTEST THING THE TALLEST PYRAMID'S SUPPORT SUPPORTS. Computer: RED Note that this is not just mindless text generation. The conversation is held in the context of a "blocks world" where a robot with a single hand and eye ("Computer" in the excerpt above) moves blocks of various shapes and colours around, as directed by a human user in free-form natural language. When the Computer says "OK" after it's directed to "put the littlest pyramid on top of it" it's because it really has grabbed the smallest pyramid in the blocks world and placed it on top of the small block in an earlier sentence, as the Person asked. The program has a memory module to keep track of what ellipses like "it", "one" etc refer to throughout the conversation. SHRDLU was a traditional program hand-crafted by a single PhD student- no machine learning, no statistical techniques. It included, among other things, a context-free grammar (!) of natural English and a planner (to control the robot's hand) all written in Lisp and PLANNER. In its limited domain, it was smarter than anything ever created with statisical NLP methods. ______________________ [1] https://en.wikipedia.org/wiki/SHRDLU https://en.wikipedia.org/wiki/SHRDLU

quotemstr 6y ago
Why is it surprising that a CFG can approximate a subset of English grammar?

ascavalcante80 6y ago

NLP is great for many things, but, from my own experience as a NLP developer, machines are not even close to understand human language. They can interpret well some kind of written speech, but they will struggle to grasp two humans speaking to each other. The progress we are make on building chatbots and vocal assistants is mainly due to the fact We are learning how to speaking to the machines, and not the contrary.

plutonorm 6y ago

I think the standard responses you see about gpt-3 - Hur durr, can't have intelligence from statistics. Hurt dur, Chinese room. Hur durr, doesn't understand semantics, it's just dumb. Ect ect ect ad nausea. They, go to show not how dumb gpt-3 is but how unthinking most people are. Just pulling from a bag of generally agreed upon notions, matching them up in simplistic ways and regurgitating.

FiberBundle 6y ago

I found the science exams results interesting and skimmed the paper [1]. They report an accuracy of >90% on the questions. What I found puzzling was that they have a section in the experimental results part where they test the robustness of the results using adverserial answer options, more specifically they used some simple heuristic to choose 4 additional answer options from the set of other questions which maximized 'confusion' for the model. This resulted in a drop of more than 40 percentage points in the accuracy of the model. I find this extremely puzzling, what do these models actually learn? Clearly they don't actually learn any scientific principles. [1] https://arxiv.org/pdf/1909.01958.pdf https://arxiv.org/pdf/1909.01958.pdf

wrs 6y ago
I would be interested in hearing the results from humans presented with adversarial answer options. You may say that a machine learning correlations between words isn’t really learning science, but I wonder how many human students aren’t either, just pretty much learning correlations between words to pass tests...
- FiberBundle 6y ago
  They do give an example of a question, in which the model chose an incorrect answer in the adversarial setting: "The condition of the air outdoors at a certain time ofday is known as (A) friction (B) light (C) force (D)weather[correct](Q) joule (R) gradient[selected](S)trench (T) add heat" I assume this might be characteristic for other questions as well, although I don't know anything about the Regents Science Exam and whether there are multiple questions about closely related topics.
  - taneq 6y ago
    That’s a terribly worded question anyway. Of the original answers, ‘weather’ is the least worst but it’s still vague.
    - mannykannot 6y ago

rvense 6y ago

I think the point about language being a model of reality was interesting. I have an MA in linguistics including some NLP from about a decade ago and was looking at a career in academic NLP. I ultimately left to become a programmer because (of life circumstances and the fact that) I didn't see much of a future for the field, precisely because it was ignoring the (to me) obvious issues of written language bias, ignorance of multi-modality and situatedness etc. that are brought up in this post. All of these results are very interesting, but I'm not really feeling like we've been proved wrong yet. There is a big question of scalability here, at least as far as the goal of AGI goes, which the author also admits: > Of course everyday language stands in a woolier relation to sheep, pine cones, desire and quarks than the formal language of chess moves stands in relation to chess moves, and the patterns are far more complex. Modality, uncertainty, vagueness and other complexities enter but the isomorphism between world and language is there, even if inexact. This woolly relation between language and reality is well-known. It has been studied in various ways in linguistics and the philosophy of language, for instance by Frege and not least Foucault and everything after. I also think many modern linguistic schools take a very different view of "uncertainty and vagueness" than I sense in the author here, but they are obviously writing for non-specialist audience and trying not to dwell on this subject here. My point is, when making and evaluating these NLP methods and the tools they are used to construct, it is extremely important to understand that language models social realities rather than any single physical one. It seems to me all too easy, coming from formal grammar or pure stats or computer science, to rush into these things with naive assumptions about what words are or how they mean things to people. I dread to think what will happen if we base our future society on tools made in that way.

skybrian 6y ago
These tools seem to be getting pretty good at fiction. In particular, playing around with AI Dungeon, it doesn't believe anything, or alternately you could say it believes everything at once. It's similar to a library that contains books by different authors, some fact, some fiction. Contradictions don't matter. Only local consistency matters, and even then, not that much. Unfortunately, many people want to believe that they are being understood. But, on the bright side, this stuff seems artistically useful? Entertainment is a big business.

skybrian 6y ago

Darn, based on the title, I was hoping for an overview of recent research. Lots of people are having fun playing with GPT-3 or AI Dungeon, myself included, but it seems like there is other interesting research going on like the REALM paper [1], [2]. What should I be reading? Why aren't people talking about REALM more? I'm no expert, but it seems like keeping the knowledge base outside the language model has a lot going for it? [1] https://ai.googleblog.com/2020/08/realm-integrating-retrieval-into.html https://ai.googleblog.com/2020/08/realm-integrating-retrieva... [2] https://arxiv.org/abs/2002.08909 https://arxiv.org/abs/2002.08909

nl 6y ago
REALM is amazing, and Google's PEGASUS[1] taught itself to count between 2 and 5 (which is mindblowing). Basically the NLP groups from Google and FB[2][3] are always worth watching. [1] https://ai.googleblog.com/2020/06/pegasus-state-of-art-model-for.html https://ai.googleblog.com/2020/06/pegasus-state-of-art-model... [2] https://ai.facebook.com/blog/covost-v2-expanding-the-largest-most-diverse-multilingual-speech-to-text-translation-data-set/ https://ai.facebook.com/blog/covost-v2-expanding-the-largest... [3] https://ai.facebook.com/blog/introducing-a-new-large-scale-dynamic-data-set-to-push-the-limits-of-natural-language-processing/ https://ai.facebook.com/blog/introducing-a-new-large-scale-d...
MiroF 6y ago
I think there's a pretty large gap between what people in academia NLP/industry labs find interesting and what the bitcoing/VC/whatever people on twitter seem to find interesting. realm is definitely cool, and i agree that techniques like this are the way forward.

_emacsomancer_ 6y ago

A bit I found rather strange, on the language-side: > This is to say the patterns in language use mirror the patterns of how things are(1). > (1)- Strictly of course only the patterns in true sentences mirror, or are isomorphic to, the arrangement of the world, but most sentences people utter are at least approximately true. Presumably this should really say something like "...but most sentences people utter are at least approximately true of their mental representation of the world."

rllin 6y ago

the thing is humans have most efficiently encoded (in detail) reality in text. humans already highlight what is worth encoding about reality. for example, you can finetune gpt-2 to have an idea of sexual biology by having it read erotica. just like how you can have a model learn the same by watching porn. but it is much more efficient to read the text, since there is much less information that is "useless"

rland 6y ago

> Models are transitive- if x models y, and y models z, then x models z. The upshot of these facts are that if you have a really good statistical model of how words relate to each other, that model is also implicitly a model of the world. This right here is a great way of putting the success of GPT-3 into context. We think GPT is smart, because when it says something eerily human-like we apply our model of the world onto what it is saying. A conversation like this: > Me: So, what happened when you fell off the balance beam? > GPT: It hurt. > Me: Why'd it hurt so bad? > GPT: The beam was high up and I feel awkwardly. > Me: Wow, that sounds awful. In this conversation, one of us is thinking far harder than the other. GPT can have conversations like this now, which is impressive. But only I can model the beam, the fall, and the physical reality. When I say "that sounds awful," I actually do a miniature physics simulation in my head, imagining losing my balance and falling off a high beam, landing, the physical pain, etc. GPT does none of that. In either case, when it asks the question or when it answers it, it is entirely ignorant of this sort of "shadow" model that's being constructed. Generalizing a bit, our "shadow" model of reality in every single domain is far more powerful than language's approximation. That's why we won't be able to use GPT to do a medical diagnosis or create a piece of architecture or whatever else people are saying it's going to do now.

ilaksh 6y ago
Right. I recently saw a podcast with Dileep George of Vicarious AI. I don't know if his specific techniques will work fully generally but at the high level he is talking about something quite similar to what you are saying in terms of grounding language understanding and having real models of the world. So I am definitely following him. More broadly there is a growing group of researchers who are working on trying to achieve better world modeling (although of course there have been many for decades, it just seems that the number who are coming from deep learning towards AGI with more emphasis on world modeling is increasing). Such as Ferran Alet. Sort of the Tenenbaum school I guess. Or Melanie Mitchell has been saying similar for quite awhile. They have recent talks on Good AI's YouTube channel.
dwohnitmok 6y ago
The author's quote is asserting the opposite of what you're saying here. Filling in "GPT" for x, "text" for y, and "world" for z, the author is stating "if GPT models text, and text models the world, then GPT models the world." In particular, the author directly addresses your point that "Generalizing a bit, our 'shadow' model of reality in every single domain is far more powerful than language's approximation." > Modality, uncertainty, vagueness and other complexities enter but the isomorphism between world and language is there, even if inexact. My own personal view tends to lean towards the author's. GPT-3 , despite its many astonishing achievements, has many many limitations. Yet I don't see any indication as of yet that any of those limitations are fundamental ones inherent to the approach behind the GPT series, which is why GPT-3 both excites and frightens me.

ragebol 6y ago

> In my head- and maybe this was naive- I had thought that, in order to attempt these sorts of tasks with any facility, it wouldn’t be sufficient to simply feed a computer lots of text. (Tasks here referring to questions in the New York Regent’s science exam) Same for me. But it makes sense of course that learning from text only is entirely possible. I certainly have not directly observed the answer to eg. 'Which process in an apple tree primarily results from cell division? (1) growth (2) photosynthesis (3) gas exchange (4) waste removal', I have been taught, from text books, what the answer should be. I do have a much better grounding of what growth is, what apples and apple trees are though.

walleeee 6y ago

> A lot of the power of the thought experiment hinges on the fact that the room solves questions using a lookup table, this stacks the deck. Perhaps we be more willing to say that the room as a whole understood language if it formed an (implicit) model of how things are, and of the current context, and used those models to answer questions. Some define intelligence (entirely separately from consciousness) precisely as the ability to develop an internal model. Coupled to a regulatory feedback the system can then modify itself in response to some set of internal and/or external conditions (Joscha Bach for instance suggests consciousness is a consequence of extremely complex self-models)

laurieg 6y ago

I find it a little bit strange that there is an unspoked assumption in almost all natural language processing: That speech and text are perfectly equivalent. All of the examples in the article work on English text, not spoken English. I would consider spoken English to be a much better "Gold standard" of natural language. I'm really looking forward to machine translation operating purely on a speech in/speech out basis, instead of converting to text as an intermediate step.

benibela 6y ago

Rather than a generator, I could use a good verifier, i.e., an accurate grammar checker