6 ms·

> It is trained to put together words with similar... meaning. So the embedding is a representation of... meaning. This might be waxing philosophical, but my p

by dvt 4y ago

> It is trained to put together words with similar... meaning. So the embedding is a representation of... meaning. This might be waxing philosophical, but my position is that the meaning of a word is intrinsic, not extrinsic. The meaning of the word "ball" has nothing to do with the fact that it's next to the word "net" on a page (which is what LLMs do). But rather, "ball" is the round rubbery thing I kick around in my back yard.

airstrike 4y ago
Except "ball" can also mean a party, so you do need "net" or other words for context
- vidarh 4y ago
  More fundamentally, until someone has told you that "ball" can mean "a round thing" or pointed to a ball and said that "ball" can mean that, "ball" does not have that meaning to you. The meaning is purely extrinsic. There's nothing about a ball that makes it named that, as shown by the fact that a ball is called other things in other languages.
travisjungroth 4y ago
Locality is just how the embeddings are created, and at that only one way. “Ball” will also be closer to “round” and “net” will be closer to “string”. So this is the creation process, but you end up with something that sure does look a lot like meaning. You can do math with these vectors, like addition. king - man + woman = queen is the classic Word2Vec example. Based on you calling that phrase earlier “word salad”, I think you’re just not that familiar with word embeddings. https://en.m.wikipedia.org/wiki/Word_embedding https://en.m.wikipedia.org/wiki/Word_embedding
- dvt 4y ago
  > but you end up with something that sure does look a lot like meaning I suggest you read some Quine and Kripke; the argument that word embeddings encode meaning (particularly via locality) is an incredibly philosophically-naive position. People have been debating what language is for a very long time.
  - travisjungroth 4y ago
    Wait, how are you holding this position and telling me what to read when you couldn’t understand what “high dimensional representation of meaning” meant a few hours ago? Maybe the thing that will land is to say that embeddings are an attempt at representing meaning. There’s lots of other stuff you could represent about words. You could represent popularity by giving each word a number between 0 and 1 representing what portion of the training text was that word. That would tell you if a text might be harder for someone new to the language to understand. You could represent the spelling of the word as a list of integers. You could represent the pronunciation difficulty with a score based on ambiguous and infrequent n-grams. Or, you could try to represent the concept that the word is pointing to so you can follow synonyms or categorize text by topic. This would be an attempt at representing the word’s meaning. I’ve been explaining as if you didn’t understand what this was, but if you disagree that’s another thing.
    - YeGoblynQueenne 4y ago
      >> Wait, how are you holding this position and telling me what to read when you couldn’t understand what “high dimensional representation of meaning” meant a few hours ago? From the conversation so far it's clear to me that the OP has at least some idea of how word embeddings work and they described as "word salad" the expression that related them to "meaning", i.e. they quibbled about the "... representation of meaning", not the "high dimensional representation... " part. I hope the OP can give a reference to the Quine and Kripke source they refer to. As far as I remember it, the point about arithmetic with word verctors (man, king, woman, queen and all that) is a motivational example for Word2Vec by Tomas Mikolov, which wikpedia tells me was published in 2013. I can't find the reference right now, but in 2014 I was taking a Master's in all that jazz and I remember one of my tutors having the paper on his desk, and commenting that the claim that word embeddings model meaning was "well, that's what he says". So there has been debate and disagreement about the ability of word embeddings to represent meaning for at least as long as Word2Vec has existed, and as far as I know the idea of word embeddings existed earlier than that (I was taught the concept unconnected to word2vec in my Master's). The bottom line is, just because somebody says their algorithm does a thing, doesn't mean that everyone has to accept it immediately, and without critical discussion. So I, for one, want to see that Quine and Kripke stuff the OP is referring to.
- zwaps 4y ago
  Embeddings can go beyond a contextual definition of meaning. You can use (transformer) LLMs to get a "word sense" aka its predicted probability distribution, which also induces a network of associations (not via context, but via whatever the LLM has learned as fitting words). This gives a structural definition of meaning the ye olde sense (v. Humboldt and so forth). This of course starts with the assumption that LLMs can encode meaning, albeit not in a simplistic linear fashion as Word2Vec or GloVe did https://arxiv.org/abs/2110.04151 https://arxiv.org/abs/2110.04151
Dylan16807 4y ago
That rubbery thing is just an example of a ball. The meaning of words is pretty abstract and mostly communicated by using other words.
vidarh 4y ago
The notion that the meaning is intrinsic is hogwash. A word without someone ascribing it meaning means nothing. A word has meaning because we give it to it, and we give it that meaning based on context. Some of that context is remote in time and space (we learned the word ball a long time ago), some is near (that it occurs near "back yard" and "round rubbery thing" makes it more likely it's that thing we use to play games rather than a testicle or a party where people dance), but it is wholly dependent on context. Put it alongside words in a different language, for example, and it might have a whole other set of potential meanings.
- YeGoblynQueenne 4y ago
  If I understand correctly, that's what the OP, and others, are claiming, that it's possible to figure out how to represent meaning by putting words next to each other, if you look for long enough, at many enough words sitting next to other words. So far this remains a matter of debate. The objection I though you were making, with the "ball" etc, is that all these words that sit next to other words according to what the words mean, must mean something in isolation. For example, even though "ball" can be used in different contexts, it can only mean so many things, and it would never mean, say, "a cooking utensil made of aluminum where I fry my eggs each morning" no matter what other words you put it next to. And, as Young et al (1976) have demonstrated, it is possible to move words out of their expected context for great fun and profit, but it's not clear that a word can always be moved in any context, and still make sense. I would even go so far as to say that can probably not be done, at least not without a drastic reconfiguration of all of English (where "ball" is used). So the question is not only "what other words do we find the word 'ball' close to" but also "why do we find 'ball' next to those other words?". The latter question can't be answered just by looking at what words hang out with what other words, unless we already know what words mean on their own. And that is, at least for me, the objection about LLMs "meaning" and "understanding" anything. No matter what text an LLM is generating, the entity that is decoding the meaning of the generating text is always a human. The LLM can't do that on its own. Because there is no mechanism that it is equipped with that could ever do that. P.S. "words hanging out with other words" are what are known as "token collocations" in technical jargon. They are an idea as old as linguistics itself, a basal concept whose modern implementations in NLP systems are just, well, modern implementations. We still don't know how humans decode meaning from token collocations even though it's such an ancient idea. And it might even be wrong, at this point. _________________ Bibliography 1. Angus Young, Malcolm Young, and Bon Scott (1976) Big Balls. In Harry Vanda, George Young (prod), Dirty Deeds Done Dirt Cheap, Albert productions - Atlantic Records, Sydney, Australia, side 2, track 2. URL: https://youtu.be/4WwJ6OVSwkM https://youtu.be/4WwJ6OVSwkM
SanderNL 4y ago
So it is not related to the textual token next to it but a token of another modality? (Like touch or sight?) I think so too, but how is that intrinsic? It’s still contextual. I think constricting it to text only is a mistake. Tokens can represent anything.

by dvt 4y ago

airstrike 4y ago

Except "ball" can also mean a party, so you do need "net" or other words for context

vidarh 4y ago
More fundamentally, until someone has told you that "ball" can mean "a round thing" or pointed to a ball and said that "ball" can mean that, "ball" does not have that meaning to you. The meaning is purely extrinsic. There's nothing about a ball that makes it named that, as shown by the fact that a ball is called other things in other languages.

travisjungroth 4y ago

Locality is just how the embeddings are created, and at that only one way. “Ball” will also be closer to “round” and “net” will be closer to “string”. So this is the creation process, but you end up with something that sure does look a lot like meaning. You can do math with these vectors, like addition. king - man + woman = queen is the classic Word2Vec example. Based on you calling that phrase earlier “word salad”, I think you’re just not that familiar with word embeddings. https://en.m.wikipedia.org/wiki/Word_embedding https://en.m.wikipedia.org/wiki/Word_embedding

dvt 4y ago
> but you end up with something that sure does look a lot like meaning I suggest you read some Quine and Kripke; the argument that word embeddings encode meaning (particularly via locality) is an incredibly philosophically-naive position. People have been debating what language is for a very long time.
- travisjungroth 4y ago
  Wait, how are you holding this position and telling me what to read when you couldn’t understand what “high dimensional representation of meaning” meant a few hours ago? Maybe the thing that will land is to say that embeddings are an attempt at representing meaning. There’s lots of other stuff you could represent about words. You could represent popularity by giving each word a number between 0 and 1 representing what portion of the training text was that word. That would tell you if a text might be harder for someone new to the language to understand. You could represent the spelling of the word as a list of integers. You could represent the pronunciation difficulty with a score based on ambiguous and infrequent n-grams. Or, you could try to represent the concept that the word is pointing to so you can follow synonyms or categorize text by topic. This would be an attempt at representing the word’s meaning. I’ve been explaining as if you didn’t understand what this was, but if you disagree that’s another thing.
  - YeGoblynQueenne 4y ago
    >> Wait, how are you holding this position and telling me what to read when you couldn’t understand what “high dimensional representation of meaning” meant a few hours ago? From the conversation so far it's clear to me that the OP has at least some idea of how word embeddings work and they described as "word salad" the expression that related them to "meaning", i.e. they quibbled about the "... representation of meaning", not the "high dimensional representation... " part. I hope the OP can give a reference to the Quine and Kripke source they refer to. As far as I remember it, the point about arithmetic with word verctors (man, king, woman, queen and all that) is a motivational example for Word2Vec by Tomas Mikolov, which wikpedia tells me was published in 2013. I can't find the reference right now, but in 2014 I was taking a Master's in all that jazz and I remember one of my tutors having the paper on his desk, and commenting that the claim that word embeddings model meaning was "well, that's what he says". So there has been debate and disagreement about the ability of word embeddings to represent meaning for at least as long as Word2Vec has existed, and as far as I know the idea of word embeddings existed earlier than that (I was taught the concept unconnected to word2vec in my Master's). The bottom line is, just because somebody says their algorithm does a thing, doesn't mean that everyone has to accept it immediately, and without critical discussion. So I, for one, want to see that Quine and Kripke stuff the OP is referring to.
zwaps 4y ago
Embeddings can go beyond a contextual definition of meaning. You can use (transformer) LLMs to get a "word sense" aka its predicted probability distribution, which also induces a network of associations (not via context, but via whatever the LLM has learned as fitting words). This gives a structural definition of meaning the ye olde sense (v. Humboldt and so forth). This of course starts with the assumption that LLMs can encode meaning, albeit not in a simplistic linear fashion as Word2Vec or GloVe did https://arxiv.org/abs/2110.04151 https://arxiv.org/abs/2110.04151

Dylan16807 4y ago

That rubbery thing is just an example of a ball. The meaning of words is pretty abstract and mostly communicated by using other words.

vidarh 4y ago

The notion that the meaning is intrinsic is hogwash. A word without someone ascribing it meaning means nothing. A word has meaning because we give it to it, and we give it that meaning based on context. Some of that context is remote in time and space (we learned the word ball a long time ago), some is near (that it occurs near "back yard" and "round rubbery thing" makes it more likely it's that thing we use to play games rather than a testicle or a party where people dance), but it is wholly dependent on context. Put it alongside words in a different language, for example, and it might have a whole other set of potential meanings.

YeGoblynQueenne 4y ago
If I understand correctly, that's what the OP, and others, are claiming, that it's possible to figure out how to represent meaning by putting words next to each other, if you look for long enough, at many enough words sitting next to other words. So far this remains a matter of debate. The objection I though you were making, with the "ball" etc, is that all these words that sit next to other words according to what the words mean, must mean something in isolation. For example, even though "ball" can be used in different contexts, it can only mean so many things, and it would never mean, say, "a cooking utensil made of aluminum where I fry my eggs each morning" no matter what other words you put it next to. And, as Young et al (1976) have demonstrated, it is possible to move words out of their expected context for great fun and profit, but it's not clear that a word can always be moved in any context, and still make sense. I would even go so far as to say that can probably not be done, at least not without a drastic reconfiguration of all of English (where "ball" is used). So the question is not only "what other words do we find the word 'ball' close to" but also "why do we find 'ball' next to those other words?". The latter question can't be answered just by looking at what words hang out with what other words, unless we already know what words mean on their own. And that is, at least for me, the objection about LLMs "meaning" and "understanding" anything. No matter what text an LLM is generating, the entity that is decoding the meaning of the generating text is always a human. The LLM can't do that on its own. Because there is no mechanism that it is equipped with that could ever do that. P.S. "words hanging out with other words" are what are known as "token collocations" in technical jargon. They are an idea as old as linguistics itself, a basal concept whose modern implementations in NLP systems are just, well, modern implementations. We still don't know how humans decode meaning from token collocations even though it's such an ancient idea. And it might even be wrong, at this point. _________________ Bibliography 1. Angus Young, Malcolm Young, and Bon Scott (1976) Big Balls. In Harry Vanda, George Young (prod), Dirty Deeds Done Dirt Cheap, Albert productions - Atlantic Records, Sydney, Australia, side 2, track 2. URL: https://youtu.be/4WwJ6OVSwkM https://youtu.be/4WwJ6OVSwkM

SanderNL 4y ago

So it is not related to the textual token next to it but a token of another modality? (Like touch or sight?) I think so too, but how is that intrinsic? It’s still contextual. I think constricting it to text only is a mistake. Tokens can represent anything.