lm-polygraph

This isn't Anthropic, but here is a python library that focuses on different ways of detecting hallucinations. https://github.com/IINemo/lm-polygraph (caveat emptor, I doubt this really works).

https://github.com/IINemo/lm-polygraph is the best work in this space

Yep, usually it's called abstention or rejection. 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. [1] https://arxiv.org/abs/2406.15627 [2] https://githu

The idea behind semantic entropy (estimating entropy of distribution over semantic units, instead of individual sequences in the output space) is great, but it's somewhat naive in the sense that it considers these semantic units to be well-defined partitions of output space. There is further generalization of this approach [1] which performs soft clustering of sampled outputs based on a similar notion of semantic equivalence between them. 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