There are a number of ways which the community has begun investigating trading off compute for reliability – including adaptively – in AI models. We will discuss a path which has been less explored given scalability and accessibility questions which we seek to resolve. By investing more compute into the probabilistic inference required to perform uncertainty quantification, we can unlock LLMs that hallucinate less, understand their own limits, and even reason with higher precision.

Robust decision making needs to handle complex uncertainty