[glymor]

TL;DR sample the top N results from the LLM and use traditional NLP to extract factoids, if the LLM is confabulating the factoids would have random distribution, but if it's not it will be heavily weighted towards one answer.

A figure from the paper shows this better than my TL;DR: https://www.nature.com/articles/s41586-024-07421-0/figures/1

[Terr_]

The LLM is already generating factoids: Things which resemble a fact without actually being one.

(See also: Androids that resemble men but aren't, asteroids that resemble stars but aren't, meteoroids that resemble meteors but aren't...)

[cl42]

Thank you! This is so helpful.

It's also interesting to see what temperature value they use (1.0, 0.1 in some cases?)... I have a feeling using the actual raw probability estimates (if available) would provide a lot of information without having to rerun the LLM or sample quite as heavily.

[visarga]

Or we could just ask the same question on 3 different LLMs, ideally a large LLM, a RAG LLM and a small one, then use LLM again to rewrite the final answer. When models contradict each other there is likely hallucination going on, but correct answers tend to converge.

[superb_dev]

Why use an LLM to check the work of a different LLM?

You could use the same technique that this paper describes to compare the answers each LLM gave. LLMs don’t have to be in opposition to traditional NLP techniques