alt Hacker NewsI don’t see how it’s obvious that LLM’s will be capable of any mathematical, “reasoning.
LLM’s can infer relationships and maintain longer context chains in order to generate their output… it still happens that some times the output is correct depending on the training data, layers, context, etc. And it can get more accurate when we change the parameters of the model. But the algorithm isn’t “doing” anything here. It will generate something regardless of what it’s prompted with.
Maybe it’s right. But the algorithm is an algorithm. It doesn’t care what truth is. It’s generating BS essentially.
A human is doing a lot more work when performing mathematics.
It may be that LLM’s can be a useful tool in mathematical reasoning but it’s not obvious that it will ever be capable of it without a human, let alone be better than a human.
I think models could be designed that in separate layers created "logical system" representations which could feed back into the output, much like how attention works. Attention is about relevance, the logical layers could be based on logical schema-based patterns.
Consider an LLM that happened to have some pre-trained layers that were trained abstractly on all the constructive proofs available for modern mathematics. LLMs with image recognition rely on existing visual pattern recognition layers, fwiw.