vark90

When people in this field compare various methods of quantifying model uncertainty, they often perform what is called rejection verification. Basically, you continuously reject data points where uncertainty is high, and see how average quality of the remaining outputs increases. A good uncertainty estimate is highly correlated with output quality, and thus low-uncertainty outputs should have higher average quality.

We use exactly this approach in our recent benchmark of uncertainty estimation approaches for LLMS [1] and have an open-source library under development [2] which allows for such benchmarking. It also can produce uncertainty scores for a given model output, so ppl in industry can integrate it into their applications as well.

But even with this in mind, there are caveats. We have recently published [2] a comprehensive benchmark of SOTA approaches to estimating uncertainty of LLMs, and have reported that while in many cases these semantic-aware methods do perform very well, in other tasks simple baselines, like average entropy of token distributions, performs on par or better than complex techniques.

We have also developed an open-source python library [3] (which is still in early development) that offers implementations of all modern UE techniques applicable to LLMs, and allows easy benchmarking of uncertainty estimation methods as well as estimating output uncertainty for deployed models in production.