LLMs could be very useful in formalizing the problem and assumptions (conversion from natural language), but once problem is described in a formal way (it can be described in some fuzzy logic), then more reliable AI techniques should be applied.

Interestingly, Tao mentions https://teorth.github.io/equational_theories/, and I believe this is better progress than LLMs doing math. I believe enhancing Lean with more tactics and formalizing those in Lean itself is a more fruitful avenue for AI in math.

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agentcoops a day ago | parent [-]

I used to work quite extensively with Isabelle and as a developer on Sledgehammer [1]. There are well-known results, most obviously the halting problem, that mean fully-automated logical methods applied to a formalism with any expressive capability, i.e. that can be used to formalize non-trivial problems, simply can never fulfill the role you seem to be suggesting. The proofs that are actually generated in that way are, anyway, horrendous -- in fact, the problem I used to work on was using graph algorithms to try and simplify computer-generated proofs for human comprehension. That's the very reason that all the serious work has previously been on proof /assistants/ and formal validation.

LLMs, especially in /conjunction/ with Lean for formal validation, are really an exciting new frontier in mathematics and it's a mistake to see that as just "unreliable" versus "reliable" symbolic AI etc. The OP Terence Tao has been pushing the edge here since day one and providing, I think, the most unbiased perspective on where things stand today, strengths as much as limitations.

[1] https://isabelle.in.tum.de/website-Isabelle2009-1/sledgehamm...

	▲	js8 a day ago \| parent [-]
		LLMs (as well as humans) are algorithms like anything else and so they are also subject to halting problem. I don't see what LLMs do that couldn't be in principle formalized as a Lean tactic. (IMHO LLMs are just learning rules - theorems of some kind of fuzzy logic - and then try to apply them using heuristic search to satisfy the goal. Unfortunately the rules learned are likely not fully consistent and so you get reasoning errors.)