fair points but i disagree. LLMs are very good about explaining and summarizing complicated material. You can test this today with any modern LLM about any topic or paper you like. One thing that makes them so powerful is that they can customize their explanation to the specific querier.
Today, to explain and communicate these math proofs, it makes sense that a human working with an LLM would be the most effective at human explanation. However, given LLMs current abilities with explanation, it seems reasonable that LLMs will become equal to human experts in explanation. Again part of their power is that they can scale to 1-on-1 explanations for the particular audience which humans cannot do.
> "it's not X it's Y" construct.
I agree this is annoying, but this is not a fundamental feature of LLMs, this is "tick" of current models, like the em-dash. There have been countless of these "ticks" that have been refined and this process will continue.
Today, to explain and communicate these math proofs, it makes sense that a human working with an LLM would be the most effective at human explanation. However, given LLMs current abilities with explanation, it seems reasonable that LLMs will become equal to human experts in explanation. Again part of their power is that they can scale to 1-on-1 explanations for the particular audience which humans cannot do.
> "it's not X it's Y" construct.
I agree this is annoying, but this is not a fundamental feature of LLMs, this is "tick" of current models, like the em-dash. There have been countless of these "ticks" that have been refined and this process will continue.