3 papers
cs.LG2026
LLMs are not (consistently) Bayesian: Quantifying internal (in)consistencies of LLMs' probabilistic beliefs
Chacha Chen, Matthew Jörke, Adam Goliński +4
Modern AI systems are being deployed in complex domains such as medicine, science, and law, where it is important that they not only produce correct answers, but also represent and…
cs.CL2025
BED-LLM: Intelligent Information Gathering with LLMs and Bayesian Experimental Design
Deepro Choudhury, Sinead Williamson, Adam Goliński +5
We propose a general-purpose approach for improving the ability of large language models (LLMs) to intelligently and adaptively gather information from a user or other external sou…
cs.CL2025
SelfReflect: Can LLMs Communicate Their Internal Answer Distribution?
Michael Kirchhof, Luca Füger, Adam Goliński +4
The common approach to communicate a large language model's (LLM) uncertainty is to add a percentage number or a hedging word to its response. But is this all we can do? Instead of…