3 ms·

I'm pretty sure that the semantics of natural language are a lot more complex than can be accounted for by these seemingly very ad-hoc translations into compara

I'm pretty sure that the semantics of natural language are a lot more complex than can be accounted for by these seemingly very ad-hoc translations into comparatively straightforward FOL formulas, as are given in this paper. A common approach for the understanding of NL semantics from a strictly formal POV is Montague semantics https://en.wikipedia.org/wiki/Montague_grammar https://en.wikipedia.org/wiki/Montague_grammar https://plato.stanford.edu/entries/montague-semantics/ https://plato.stanford.edu/entries/montague-semantics/ - even a cursory look at these references is enough to clarify the level of complexity that's involved. Very loosely speaking one generally has to work with multiple "modalities" at the same time each of which, when understood from the POV of ordinary FOL, introduces its own separate notion of abstract "possible worlds" (representing, e.g. an agent's set of beliefs) and ways in which these "worlds" can relate to one another. More complex cases will usually degenerate in some sort of very generic "game semantics" https://en.wikipedia.org/wiki/Game_semantics https://en.wikipedia.org/wiki/Game_semantics https://plato.stanford.edu/entries/logic-games/ https://plato.stanford.edu/entries/logic-games/ where any given use of natural language is merely seen as a "game" (in the abstract strategic, game-theoretical sense) with its own set of possibly very ad-hoc 'rules'. The philosopher Ludwig Wittgenstein https://en.wikipedia.org/wiki/Ludwig_Wittgenstein https://en.wikipedia.org/wiki/Ludwig_Wittgenstein https://plato.stanford.edu/entries/wittgenstein/ https://plato.stanford.edu/entries/wittgenstein/ gave quite a good description of both of these approaches (from a very naïve approach based on a supposedly straightforward translation to some kind of abstract logic, to a far richer one based on notions of strategies and games) to a "formal" understanding of natural language, throughout his extensive philosophical inquiry. Which is to say, I'm not sure how this paper's results are generally expected to be all that useful in practice.

lapcat 2y ago
See also for example V.H. Dudman on the interpretation of "If" sentences: https://www.scribd.com/document/478756656/Dudman-1984-Conditional-Interpretations-of-If-sentences https://www.scribd.com/document/478756656/Dudman-1984-Condit...
cs702 2y ago
It could be useful for domains in which all or at least many problems are solvable (i.e., they can be stated and satisfied) with first-order logic. It could also be useful as a lower-level component of general-purpose systems that internally rely on chains of thought computed by sub-component LLMs.
- xhevahir 2y ago
  It wouldn't be useful if, as the parent comment is saying, it won't do a decent job of translating natural language.
  - cs702 2y ago
    Ah, got it. Thanks!
tgv 2y ago
I've worked on classical NLP models for quite some time, and this indeed looks way too simple to be of any practical use. If you mention Montague, I'm going to refer you to "Pedro owns a donkey," the poster kid sentence for Discourse Representation Theory. That's 1980s work, and for simple sentences it's already complicated beyond what the OP article suggests, and fails on anything remotely complex. I think it goes 2nd order the moment a complement is introduced (I think that ...). And even if you can translate a sentence into a predicate, you haven't begun understanding what lies behind all those predicates. E.g., "Zelensky is ready to work under Trump's 'strong leadership' after 'regrettable' showdown." What good does it do to have that in FOP? [1] https://plato.stanford.edu/archIves/sum2011/entries/discourse-representation-theory/ https://plato.stanford.edu/archIves/sum2011/entries/discours...
- zozbot234 2y ago
  It looks like classic models of NLP semantics mostly punt on the "logical" point of view precisely due to these difficulties, and focus mostly on the more surface level problem of describing how each word of the source text correlates with a deeper description of the "meaning" of the text as a whole. So it is simply assumed that the meaning of the text as a whole must be derived compositionally from the meaning of each part (usually described by a somewhat ad-hoc "frame" structure), but exactly what that entails in a "logical" sense is left unspecified. UMR (Universal Meaning Representations) seems to be a typical example of such a system https://github.com/umr4nlp/umr-guidelines/blob/master/guidelines.md https://github.com/umr4nlp/umr-guidelines/blob/master/guidel... The expected use case seems to be something like building a common intermediate language for an automated translation system; individual meaning elements can then be "mapped" in a useful way, even across different languages, but there's not much interest apparently in "inferring" further knowledge from what one already has, or even on verifying that any given inference is valid (as proposed by OP).
- WaxProlix 2y ago
  Even beyond that you have a ton of pragmatics post-grice to deal with. Computing implicatures is complex and requires a lot of knowledge about context etc. The truth value of a statement and the 'truth value' of a speech act are pretty different things - not sure it's really feasible to convert between them.
a-dub 2y ago
would be interesting if they had adversarial/null llms attempting the noisy nlp reductions as well. then one could make arguments about the sturdiness of the noisy bit.
thomastjeffery 2y ago
Text that is written in Natural Language is open to interpretation. There are many formal statements that can be said to interpret a given Natural Language text. Can we determine which formal representation is correct? What about most useful? The obvious answer to these questions is, "no". There is no such thing as a conclusive interpretation. If there was, then Natural Language wouldn't be ambiguous in the first place! So we're all doomed to constantly misinterpret each other forever, right? No? We humans use Natural Language all the time, and usually figure out what the other person actually means!? How do we do it? Are we all just really good at guessing? No, we have something better: context. Context exists both in and around Natural Language text. Context determines which formal meaning is used to interpret the text. If we don't know which context is appropriate, there may be clues in the text itself that help us construct one that is useful or correct. --- I've been trying to work out an approach to language processing that interprets text into logical formalisms (arbitrary meaning). I call them "Stories". A Story is an arbitrary interpretation of text. A Story is never conclusive: instead it is used as arbitrary context to interpret the next text. I call this process "Backstory". We could even do the process backwards, and "write" an arbitrary formalism (meaning) in the same language/style/voice as a previously interpreted Story. Given enough example instances of Story, we should be able to read and write to each other through explicitly shared context. I call this process "Empathizing". I call my idea the Story Empathizer. I'm definitely out of my depth when it comes to the details, though...
- pylotlight 2y ago
  I find humans have variation in ability for this as well though. Like some people need waaay more context, and need everything spelled out in granular detail to understand a topic, vs others who can more easily adapt, pick up clues and other relevant context information.
  - thomastjeffery 2y ago
    That's definitely true. I also suspect that holding too much potential context can be counterproductive, because then you have too many options to choose from. This happens a lot with jokes: there are a lot of unique backstories offered by different pop culture references, and pop culture is quickly diversifying to an overwhelming size. There is a lot of entropy in human expression. The good news is that context can sometimes merge stories together. When we do explicitly find shared context, we tend to leverage that knowledge. My idea is about offloading as much of this process as possible to a computer. We would still need to choose backstories, but the rest could be done in plain view, leveraging the incredible speed and memory size computers have.
avodonosov 2y ago
Your argumets and links are interesting, I hope to study these materials some day. But. To be useful in practice the approach does not need to work in all cases of natural language usage. Even if works in some limited cases there may be useful applications. The authors evaluate their approach on two datasets. One is LOGIC consisting of learning examples of logical fallacies. The other is LOGICCLIMATE, consisting of logical fallacies collected from real world news articles about climate change. The datasets are here, if anyone is interested to see the type of natural language they try to adress currently: https://github.com/causalNLP/logical-fallacy https://github.com/causalNLP/logical-fallacy I guess this csv contains the LOGICCLIMATE: https://github.com/causalNLP/logical-fallacy/blob/main/data/climate_all.csv https://github.com/causalNLP/logical-fallacy/blob/main/data/... So a possible practicle utility for the approach - spot individual wrong sentences in a long article and highlight them. Another real world example. I propose a solution at work, based on some statistics. And a colleague dismisses it by saying that there is a book "6 Ways to Lie with Statistics". If there was a smart assistant in the room who gently explained his logical fallacy to the colleague, it would save a lot of efforts for me and made the discusdion more productive. I doubt the difficulties you mention apply to this simple case.
- nickpsecurity 2y ago
  "And a colleague dismisses it by saying that there is a book "6 Ways to Lie with Statistics"." Except, that's going in the right direction towards a better argument: empiricism requires your statistics to be peer reviewed for errors or deception before being believed. That takes a skilled individual. So, you either think they're very good at statistics or you want them to put faith in your work. Otherwise, they need a smart assistant they trust to review the statistics. Then, they have increased confidence in your solution but it still might be wrong.
  - avodonosov 2y ago
    He was not calling for better statistics, he suggested to ignore statistics. It was a simple case and actually I was not presenting a statistics I collected, I just suggested to try using some numerical evidence to chose a decision. On another occasion I mentioned to somebody that it's necessary to chose drugs or medical approaches verified with medical trials and double blind method. And they replied that there is a book about how to lie with statistics and continued to consider unverified methods. I mean that in real life sometimes very simple fallacies happan. Some statistics-based deсisions may be wrong => right decision must avoid statistics. These cases could probably be adressed with automated tools of the near future.
andrewdb 2y ago
One way to slightly mitigate the difficulties of nuance in language when translating to formal arguments is to attwmpt to always steelman the argument. Afford it all the guarded language and nuance you can, and then formalize in premises and conclusion. This would also make interaction much more civil as well, given so much proclivity to do the opposite (straw man). It's not a perfect approach, but it helps. LLMs are quite decent at steelmanning as well, because they can easiky pivot language to caveat and decorate with nuamce.
da_chicken 2y ago
I don't think that's the reason it won't be very useful. I think there are two reasons it won't be very useful: 1. Most natural language arguments are not sound because the argument is not deductive logic. Most natural language arguments are persuasive, not formal reasoning. 2. Formal logic is method of preserving truth. It doesn't really create truth. That makes it a lot less useful. Critically, while a deductively valid argument has a true conclusion if all the premises are true, an invalid argument can still have a true conclusion. Formal logic, then, is very narrow. This is why finding a logical fallacy in an argument is often not convincing by itself. It doesn't say "your logic is flawed therefore I am right". It says "your logic is flawed and therefore should be revised and improved."
- bwfan123 2y ago
  > Most natural language arguments are not sound because the argument is not deductive logic. Most natural language arguments are persuasive, not formal reasoning related notes that there is some evidence that "Language is primarily a tool for communication rather than thought" [1]. ie, that language is neither necessary nor sufficient for the so-called psychic thinking process. It serves as a communication mechanism. Meanwhile, there is a hypothesis that the psychic thinking process lies beyond computation as we know it [2] in the form of turing machines etc. [1] https://www.nature.com/articles/s41586-024-07522-w https://www.nature.com/articles/s41586-024-07522-w [2] https://www.amazon.com/Emperors-New-Mind-Concerning-Computers/dp/0192861980 https://www.amazon.com/Emperors-New-Mind-Concerning-Computer...

lapcat 2y ago

See also for example V.H. Dudman on the interpretation of "If" sentences: https://www.scribd.com/document/478756656/Dudman-1984-Conditional-Interpretations-of-If-sentences https://www.scribd.com/document/478756656/Dudman-1984-Condit...

cs702 2y ago

It could be useful for domains in which all or at least many problems are solvable (i.e., they can be stated and satisfied) with first-order logic. It could also be useful as a lower-level component of general-purpose systems that internally rely on chains of thought computed by sub-component LLMs.

xhevahir 2y ago
It wouldn't be useful if, as the parent comment is saying, it won't do a decent job of translating natural language.
- cs702 2y ago
  Ah, got it. Thanks!

tgv 2y ago

I've worked on classical NLP models for quite some time, and this indeed looks way too simple to be of any practical use. If you mention Montague, I'm going to refer you to "Pedro owns a donkey," the poster kid sentence for Discourse Representation Theory. That's 1980s work, and for simple sentences it's already complicated beyond what the OP article suggests, and fails on anything remotely complex. I think it goes 2nd order the moment a complement is introduced (I think that ...). And even if you can translate a sentence into a predicate, you haven't begun understanding what lies behind all those predicates. E.g., "Zelensky is ready to work under Trump's 'strong leadership' after 'regrettable' showdown." What good does it do to have that in FOP? [1] https://plato.stanford.edu/archIves/sum2011/entries/discourse-representation-theory/ https://plato.stanford.edu/archIves/sum2011/entries/discours...

zozbot234 2y ago
It looks like classic models of NLP semantics mostly punt on the "logical" point of view precisely due to these difficulties, and focus mostly on the more surface level problem of describing how each word of the source text correlates with a deeper description of the "meaning" of the text as a whole. So it is simply assumed that the meaning of the text as a whole must be derived compositionally from the meaning of each part (usually described by a somewhat ad-hoc "frame" structure), but exactly what that entails in a "logical" sense is left unspecified. UMR (Universal Meaning Representations) seems to be a typical example of such a system https://github.com/umr4nlp/umr-guidelines/blob/master/guidelines.md https://github.com/umr4nlp/umr-guidelines/blob/master/guidel... The expected use case seems to be something like building a common intermediate language for an automated translation system; individual meaning elements can then be "mapped" in a useful way, even across different languages, but there's not much interest apparently in "inferring" further knowledge from what one already has, or even on verifying that any given inference is valid (as proposed by OP).
WaxProlix 2y ago
Even beyond that you have a ton of pragmatics post-grice to deal with. Computing implicatures is complex and requires a lot of knowledge about context etc. The truth value of a statement and the 'truth value' of a speech act are pretty different things - not sure it's really feasible to convert between them.

a-dub 2y ago

would be interesting if they had adversarial/null llms attempting the noisy nlp reductions as well. then one could make arguments about the sturdiness of the noisy bit.

thomastjeffery 2y ago

Text that is written in Natural Language is open to interpretation. There are many formal statements that can be said to interpret a given Natural Language text. Can we determine which formal representation is correct? What about most useful? The obvious answer to these questions is, "no". There is no such thing as a conclusive interpretation. If there was, then Natural Language wouldn't be ambiguous in the first place! So we're all doomed to constantly misinterpret each other forever, right? No? We humans use Natural Language all the time, and usually figure out what the other person actually means!? How do we do it? Are we all just really good at guessing? No, we have something better: context. Context exists both in and around Natural Language text. Context determines which formal meaning is used to interpret the text. If we don't know which context is appropriate, there may be clues in the text itself that help us construct one that is useful or correct. --- I've been trying to work out an approach to language processing that interprets text into logical formalisms (arbitrary meaning). I call them "Stories". A Story is an arbitrary interpretation of text. A Story is never conclusive: instead it is used as arbitrary context to interpret the next text. I call this process "Backstory". We could even do the process backwards, and "write" an arbitrary formalism (meaning) in the same language/style/voice as a previously interpreted Story. Given enough example instances of Story, we should be able to read and write to each other through explicitly shared context. I call this process "Empathizing". I call my idea the Story Empathizer. I'm definitely out of my depth when it comes to the details, though...

pylotlight 2y ago
I find humans have variation in ability for this as well though. Like some people need waaay more context, and need everything spelled out in granular detail to understand a topic, vs others who can more easily adapt, pick up clues and other relevant context information.
- thomastjeffery 2y ago
  That's definitely true. I also suspect that holding too much potential context can be counterproductive, because then you have too many options to choose from. This happens a lot with jokes: there are a lot of unique backstories offered by different pop culture references, and pop culture is quickly diversifying to an overwhelming size. There is a lot of entropy in human expression. The good news is that context can sometimes merge stories together. When we do explicitly find shared context, we tend to leverage that knowledge. My idea is about offloading as much of this process as possible to a computer. We would still need to choose backstories, but the rest could be done in plain view, leveraging the incredible speed and memory size computers have.

avodonosov 2y ago

Your argumets and links are interesting, I hope to study these materials some day. But. To be useful in practice the approach does not need to work in all cases of natural language usage. Even if works in some limited cases there may be useful applications. The authors evaluate their approach on two datasets. One is LOGIC consisting of learning examples of logical fallacies. The other is LOGICCLIMATE, consisting of logical fallacies collected from real world news articles about climate change. The datasets are here, if anyone is interested to see the type of natural language they try to adress currently: https://github.com/causalNLP/logical-fallacy https://github.com/causalNLP/logical-fallacy I guess this csv contains the LOGICCLIMATE: https://github.com/causalNLP/logical-fallacy/blob/main/data/climate_all.csv https://github.com/causalNLP/logical-fallacy/blob/main/data/... So a possible practicle utility for the approach - spot individual wrong sentences in a long article and highlight them. Another real world example. I propose a solution at work, based on some statistics. And a colleague dismisses it by saying that there is a book "6 Ways to Lie with Statistics". If there was a smart assistant in the room who gently explained his logical fallacy to the colleague, it would save a lot of efforts for me and made the discusdion more productive. I doubt the difficulties you mention apply to this simple case.

nickpsecurity 2y ago
"And a colleague dismisses it by saying that there is a book "6 Ways to Lie with Statistics"." Except, that's going in the right direction towards a better argument: empiricism requires your statistics to be peer reviewed for errors or deception before being believed. That takes a skilled individual. So, you either think they're very good at statistics or you want them to put faith in your work. Otherwise, they need a smart assistant they trust to review the statistics. Then, they have increased confidence in your solution but it still might be wrong.
- avodonosov 2y ago
  He was not calling for better statistics, he suggested to ignore statistics. It was a simple case and actually I was not presenting a statistics I collected, I just suggested to try using some numerical evidence to chose a decision. On another occasion I mentioned to somebody that it's necessary to chose drugs or medical approaches verified with medical trials and double blind method. And they replied that there is a book about how to lie with statistics and continued to consider unverified methods. I mean that in real life sometimes very simple fallacies happan. Some statistics-based deсisions may be wrong => right decision must avoid statistics. These cases could probably be adressed with automated tools of the near future.

andrewdb 2y ago

One way to slightly mitigate the difficulties of nuance in language when translating to formal arguments is to attwmpt to always steelman the argument. Afford it all the guarded language and nuance you can, and then formalize in premises and conclusion. This would also make interaction much more civil as well, given so much proclivity to do the opposite (straw man). It's not a perfect approach, but it helps. LLMs are quite decent at steelmanning as well, because they can easiky pivot language to caveat and decorate with nuamce.

da_chicken 2y ago

I don't think that's the reason it won't be very useful. I think there are two reasons it won't be very useful: 1. Most natural language arguments are not sound because the argument is not deductive logic. Most natural language arguments are persuasive, not formal reasoning. 2. Formal logic is method of preserving truth. It doesn't really create truth. That makes it a lot less useful. Critically, while a deductively valid argument has a true conclusion if all the premises are true, an invalid argument can still have a true conclusion. Formal logic, then, is very narrow. This is why finding a logical fallacy in an argument is often not convincing by itself. It doesn't say "your logic is flawed therefore I am right". It says "your logic is flawed and therefore should be revised and improved."

bwfan123 2y ago
> Most natural language arguments are not sound because the argument is not deductive logic. Most natural language arguments are persuasive, not formal reasoning related notes that there is some evidence that "Language is primarily a tool for communication rather than thought" [1]. ie, that language is neither necessary nor sufficient for the so-called psychic thinking process. It serves as a communication mechanism. Meanwhile, there is a hypothesis that the psychic thinking process lies beyond computation as we know it [2] in the form of turing machines etc. [1] https://www.nature.com/articles/s41586-024-07522-w https://www.nature.com/articles/s41586-024-07522-w [2] https://www.amazon.com/Emperors-New-Mind-Concerning-Computers/dp/0192861980 https://www.amazon.com/Emperors-New-Mind-Concerning-Computer...