Came across this recently - Semantic Uncertainty: Linguistic Invariances for Uncertainty Estimation in Natural Language Generation

Very cool, thanks!

In all honesty, at some point, any type of evaluation that is not qualitative is simply a joke. I have observed it a long time ago while working on NMT and trying to base the results on BLEU score - it literally meant nothing. Trying to force new metrics based on simple rules or computation will probably fail - I believe we need humans or stronger LLMs in the loop. E.g., humans should rank the output of multiple LLMs and the same humans should do so for multiple different language models, not just for the new one. Otherwise, I view it as a meaningless self-promoting paper (LLMs are not interesting enough to read about if there are no new ideas and no better performance). Entropy is good for language models that are like "me language model me no understand world difficult hard", not GPT-3 like.

Edit: this semantic uncertainty looks interesting but I would still rather let humans rank the results.

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That's a good point

What sounds like an open problem statement is how to get these LLMs to "quantify" that themselves the same way humans do. It's also interesting how that relates to the broader question of sentience and consciousness.

along with each prompt, just put: "And at the end of your response, state on a scale from one to ten how confident you are in you answer"

This works amazingly and is very accurate. source

It has the added bonus where you can get confidence intervals on your confidence intervals just by asking how confident it is in it's estimation of its confidence.

I am not in the language community, but in general, I dont think there is the 'best' way for uncertainty measure. In my opinion, the research on uncertainty and out-of-distribution (detection) is still very primitive and without a solid theoretical ground. For a general reference, please have a look at a recent ICLR paper.

I’ve heard you can even ask the LLM: what fraction of your uncertainty is aleatoric vs epistemic, and how would the uncertainty estimates changed if you used bootstrap vs MC dropout :)