[pgspaintbrush]

Hmm, yea, I agree with you on several points. For one, we don't fully understand the internal mechanisms of LLMs. I'm also with you on Markov chains and autocomplete tools not having an understanding of the underlying concepts. They merely use statistical patterns in the data.

Based on what you've said, it sounds like your take is that unless we can specify the exact mechanism by which LLMs understand, we have no business saying that they understand. In a lot of cases, this is a reasonable approach. In many areas, if someone tells you X, and you ask for a mechanism of action, and they can't produce one, you have solid grounds for thinking they're bullshitting.

But this case isn't quite the same. We know that LLMs learn to represent their inputs in a high-dimensional vector space (embeddings) and learn the relationships between those vectors. We also see them effectively solve problems in a variety of domains using this representation. I think these two ingredients: having a semantic representation and being able to effectively solve problems amount to something like "understanding." The lack of both properties is why I'd say Markov chains and autocomplete tools don't "understand" -- they haven't learned an effective representation of the underlying phenomena. (I'd also argue this is similar to us as humans. We don't have a good understanding of the human brain or precise mechanisms of action underlying thought. All we know is we as humans have semantic representations and can effectively solve problems.)

small note on your chess point: it now looks like chat gpt 3.5 can achieve draws against stockfish 8: https://marginalrevolution.com/marginalrevolution/2023/06/th...

bigger note on your chess point: this example illustrates that LLMs are "semi-decidable." We thought they were bad at chess, but we just hadn't discovered the right way to prompt. More generally, we can confirm when an LLM is good at X because we feed it a prompt that produces performance in X, but given the size of the input space we're dealing with here, we can't confirm that LLMs are bad at X just because we haven't seen them do well at it. Maybe we just haven't discovered the right prompt. (These input spaces are massive, by the way. ChatGPT-3.5, for example, has a context window of 4,096 tokens, so if we were considering only the English alphabet, we're looking at more than 26^{4,096} possible inputs.)

[lsy]

Two points in response to this:

I think it's a category error to call word embedding in a vector space "semantic" representation when discussing concepts like understanding. Semantics deals with the referents of words, but in this case there are no referents, merely a list of representational tokens which are defined as being "close in meaning" to the original due to proximity in text or some other structural characteristic. We call the embedding "semantic" because it is useful for human semantic purposes as we can mechanize some translations from one vector to another and receive a useful response that we then assign meaning to, but that usefulness doesn't indicate that the machine itself has any access to the referents of the tokens it's processing or semantic understanding. Put more simply, "semantics" does not merely mean the relationship between several ungrounded tokens, but that is all a vector embedding can accomplish.

Secondly, I think in the chess thread, the prompt being "engineered" in the example is extremely complex and constrains the output space sufficiently to produce high-quality results, but you start to wonder at what point the LLM is not doing most of the work. Meanwhile deeper in the thread we learn that even this prompting is not reliable and occasionally requires giving feedback that the move was bad(!) and repetition to achieve good results "the majority of the time in less than 3 tries". You can see where the practical problem arises, if we want to rely on LLMs for answers we don't already know. Claiming that we have a "general" function that "just" requires arbitrarily varying the input over an uncountably large space until you achieve the desired result is akin to saying f(x) = rand() * x is a universal computer as long as you find the right x. The ad absurdum version of the chess example is running Stockfish, sending a prompt that contains the Stockfish move and a request to repeat it, and then claiming that the LLM draws against Stockfish. However as we have seen with tokens like "SolidGoldMagikarp", LLMs are not even yet capable of reliably implementing the identity function, so I am not sure we can even say this.