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Maybe you and I have different definitions of "understand" but this seems clearly false to me.
ETA:
Perhaps better to say, I don't see what this statement has to do with LLMs.
Let me break it down:
LLMs understand words and sentences (or at least the relationships between them).
If a concept can be expressed in words, then what that concept means is encoded by how the words that express that concept relate to the words that express any other concept.
Therefore, as an LLMs capability to relate words and sentences to each other approaches or exceeds human level, the LLMs understanding of concepts approaches or exceeds human level.
This is a very high-level explanation, but I think it captures the core intuition.
I must ask, what is the most powerful LLM you have personally interacted with? Default ChatGPT feels like talking to a 6-year-old after using Bing and GPT-4.
I guess (1) is where I disagree. I think there's a substantial difference between understanding the meaning of a word and understanding that word's statistical relation to other words. LLM's understand the latter, but not the former.
I've used Bing and it's still pretty easy to ask it questions that demonstrate it doesn't understand the semantic content of what it is being asked. Here's an interaction I had just now:
I asked it the same question three times and never once did it give me a story that actually had fifteen words in it. It clearly does not understand what "fifteen" means and how the presence of the word in the my sentence should constrain its answer.
Hmmm, you're right. GPT-4 does in fact have trouble writing 15-word stories.
On the other hand, I was able to get it to write a 68 word story on the first try.
Trying this experiment with multiple 2-digit numbers X, it looks like GPT-4 outputs a story with X ± 2 words. I have updated slightly against GPT-4's capabilities (I expected it to get the exact answer most of the time), but I still hold to my original thesis. This is a quantitative error, not a qualitative error. The presence of the number in the input sentence does in fact constrain the output. Asking for a story with 68 words gives an output with more words than asking for a story with 49 words, but less words than asking for a story with 96 words. The model does have some concept of what these numbers are.
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All that is trivially shown false by how LLMs persistently fail at the most basic mathematical problems as soon as solving those would require understanding instead of just stringing words together. They are very efficient bullshit generators but to claim they "understand" anything is a massive exaggeration.
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