[wisnesky]

There is a partial solution to this problem: use formal methods such as symbolic logic and theorem proving to check the LLM output for correctness. We are launching a semantic validator for LLM-generated SQL code at sql.ai even now. (It checks for things like missing joins.) And others are using logic and math to create LLMs that don't hallucinate or have safety nets for hallucination, such as Symbolica. It is only when the LLM output doesn't have a correct answer that the technical issues become complicated.

[swatcoder]

Proofs can ensure soundness for a collection of logical statements in an output, but people are being sold epistemic "truth".

This article is trying to elaborate what that means for LLM's, which only know truth through frequency ("crowdsourced truth") at best. For esoteric, sparse, ambiguous, uncertain, controversial, etc subjects, that's not an adequate truth standard to start from and logical proofs do nothing to improve on it.

[brcmthrowaway]

Q* will have this