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What do you mean by "understand chess"? I think you don't appreciate how good the level of chess displayed here is. It would take an average adult years of ded

What do you mean by "understand chess"? I think you don't appreciate how good the level of chess displayed here is. It would take an average adult years of dedicated practice to get to 1800. The article doesn't say how often the LLM fails to generate legal moves in ten tries, but it can't be often or the level of play would be much much much worse. As seems often the case, the LLM seems to have a brilliant intuition, but no precise rigid "world model". Of course words like intuition are anthropomorphic. At best a model for what LLMs are doing. But saying "they don't understand" when they can do _this well_ is absurd.

ChoHag 2y ago
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vundercind 2y ago
> I think you don't appreciate how good the level of chess displayed here is. It would take an average adult years of dedicated practice to get to 1800. Since we already have programs that can do this, that definitely aren’t really thinking and don’t “understand” anything at all, I don’t see the relevance of this part.
- throw310822 2y ago
  It seems you're shifting the discourse here. In the context of LLMs, "to understand" is short for "to have a world model beyond the pure relations between words". In that sense, chess engines do "understand" chess, as they operate on a world model. You can even say that they don't understand anything but chess, which makes them extremely un-intelligent and definitely not capable of understanding as we mean it. However, since an LLM is a generalist engine, if it understands chess there is no reason for it not to understand millions of other concepts and how they relate to each other. And this is the kind of understanding that humans do.
photonthug 2y ago
> But saying "they don't understand" when they can do _this well_ is absurd. When we talk about understanding a simple axiomatic system, understanding means exactly that the entirety of the axioms are modeled and applied correctly 100% of the time. This is chess, not something squishy like literary criticism. There’s no need to debate semantics at all. One illegal move is a deal breaker Undergraduate CS homework for playing any game with any technique would probably have the stipulation that any illegal move disqualifies the submission completely. Whining that it works most of the time would just earn extra pity/contempt as well as an F on the project. We can argue whether an error rate of 1 in a million means that it plays like a grandmaster or a novice, but that’s less interesting. It failed to model a simple system correctly, and a much shorter/simpler program could do that. Doesn’t seem smart if our response to this as an industry is to debate semantics, ignore the issue, and work feverishly to put it to work modeling more complicated / critical systems.
- fxnn 2y ago
  > When we talk about understanding a simple axiomatic system, understanding means exactly that the entirety of the axioms are modeled and applied correctly 100% of the time. Yes, but then, when we talk about understanding in LLMs, we talk about existence, but not necessarily about determinism. Remember that, while chess engines are (I guess?) deterministic systems, LLMs are randomized systems. You give the same context and the same prompt multiple times, and each and every time you get a different response. To me, this, together with the fact that you have an at least 1-in-10 chance of getting a good move (even for strange scenarios), means that understanding _does exist_ inside the LLM. And the problem following from this is, how to force the LLM to reliably choose the right „paths of thought“ (sorry for that metaphor).
- Certhas 2y ago
  You just made up a definition of "understand". According to that definition, you are of course right. I just don't think it's a reasonable definition. It's also contradicted by the person I was replying to in the sibling comment, where they argue that Stockfish doesn't understand chess, despite Stockfish of course having the "axioms" modeled and applied correctly 100% of the time. Here are things people say: Magnus Carlsen has a better understanding of chess than I do. (Yet we both know the precise rules of the game.) Grandmasters have a very deep understanding of Chess, despite occasionally making illegal moves that are not according to the rules (https://www.youtube.com/watch?v=m5WVJu154F0 https://www.youtube.com/watch?v=m5WVJu154F0). "If AlphaZero were a conventional engine its developers would be looking at the openings which it lost to Stockfish, because those indicate that there's something Stockfish understands better than AlphaZero." (https://chess.stackexchange.com/questions/23206/the-games-alphazero-lost https://chess.stackexchange.com/questions/23206/the-games-al...) > Undergraduate CS homework for playing any game with any technique would probably have the stipulation that any illegal move disqualifies the submission completely. Whining that it works most of the time would just earn extra pity/contempt as well as an F on the project. How exactly is this relevant to the question whether LLMs can be said to have some understanding of chess? Can they consistently apply the rules when game states are given in pgn? No. _Very_ few humans without specialized training could either (without using a board as a tool to keep track of the implicit state). They certainly "know" the rules (even if they can't apply them) in the sense that they will state them correctly if you ask them to. I am not particularly interested in "the industry". It's obvious that if you want a system to play chess, you use a chess engine, not an LLM. But I am interested in what their chess abilities teaches us about how LLMs build world models. E.g.: https://aclanthology.org/2024.acl-srw.48/ https://aclanthology.org/2024.acl-srw.48/

ChoHag 2y ago

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deleted 2y ago

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vundercind 2y ago

> I think you don't appreciate how good the level of chess displayed here is. It would take an average adult years of dedicated practice to get to 1800. Since we already have programs that can do this, that definitely aren’t really thinking and don’t “understand” anything at all, I don’t see the relevance of this part.

throw310822 2y ago
It seems you're shifting the discourse here. In the context of LLMs, "to understand" is short for "to have a world model beyond the pure relations between words". In that sense, chess engines do "understand" chess, as they operate on a world model. You can even say that they don't understand anything but chess, which makes them extremely un-intelligent and definitely not capable of understanding as we mean it. However, since an LLM is a generalist engine, if it understands chess there is no reason for it not to understand millions of other concepts and how they relate to each other. And this is the kind of understanding that humans do.

photonthug 2y ago

> But saying "they don't understand" when they can do _this well_ is absurd. When we talk about understanding a simple axiomatic system, understanding means exactly that the entirety of the axioms are modeled and applied correctly 100% of the time. This is chess, not something squishy like literary criticism. There’s no need to debate semantics at all. One illegal move is a deal breaker Undergraduate CS homework for playing any game with any technique would probably have the stipulation that any illegal move disqualifies the submission completely. Whining that it works most of the time would just earn extra pity/contempt as well as an F on the project. We can argue whether an error rate of 1 in a million means that it plays like a grandmaster or a novice, but that’s less interesting. It failed to model a simple system correctly, and a much shorter/simpler program could do that. Doesn’t seem smart if our response to this as an industry is to debate semantics, ignore the issue, and work feverishly to put it to work modeling more complicated / critical systems.

fxnn 2y ago
> When we talk about understanding a simple axiomatic system, understanding means exactly that the entirety of the axioms are modeled and applied correctly 100% of the time. Yes, but then, when we talk about understanding in LLMs, we talk about existence, but not necessarily about determinism. Remember that, while chess engines are (I guess?) deterministic systems, LLMs are randomized systems. You give the same context and the same prompt multiple times, and each and every time you get a different response. To me, this, together with the fact that you have an at least 1-in-10 chance of getting a good move (even for strange scenarios), means that understanding _does exist_ inside the LLM. And the problem following from this is, how to force the LLM to reliably choose the right „paths of thought“ (sorry for that metaphor).
Certhas 2y ago
You just made up a definition of "understand". According to that definition, you are of course right. I just don't think it's a reasonable definition. It's also contradicted by the person I was replying to in the sibling comment, where they argue that Stockfish doesn't understand chess, despite Stockfish of course having the "axioms" modeled and applied correctly 100% of the time. Here are things people say: Magnus Carlsen has a better understanding of chess than I do. (Yet we both know the precise rules of the game.) Grandmasters have a very deep understanding of Chess, despite occasionally making illegal moves that are not according to the rules (https://www.youtube.com/watch?v=m5WVJu154F0 https://www.youtube.com/watch?v=m5WVJu154F0). "If AlphaZero were a conventional engine its developers would be looking at the openings which it lost to Stockfish, because those indicate that there's something Stockfish understands better than AlphaZero." (https://chess.stackexchange.com/questions/23206/the-games-alphazero-lost https://chess.stackexchange.com/questions/23206/the-games-al...) > Undergraduate CS homework for playing any game with any technique would probably have the stipulation that any illegal move disqualifies the submission completely. Whining that it works most of the time would just earn extra pity/contempt as well as an F on the project. How exactly is this relevant to the question whether LLMs can be said to have some understanding of chess? Can they consistently apply the rules when game states are given in pgn? No. _Very_ few humans without specialized training could either (without using a board as a tool to keep track of the implicit state). They certainly "know" the rules (even if they can't apply them) in the sense that they will state them correctly if you ask them to. I am not particularly interested in "the industry". It's obvious that if you want a system to play chess, you use a chess engine, not an LLM. But I am interested in what their chess abilities teaches us about how LLMs build world models. E.g.: https://aclanthology.org/2024.acl-srw.48/ https://aclanthology.org/2024.acl-srw.48/