7 citations · 9 across the 11 of their papers we have counts for
4 papers · 1 filter
BAS: A Decision-Theoretic Approach to Evaluating Large Language Model Confidence
Sean Wu, Fredrik K. Gustafsson, Edward Phillips +3
Large language models (LLMs) often produce confident but incorrect answers in settings where abstention would be safer. Standard evaluation protocols, however, require a response a…
Entropy Alone is Insufficient for Safe Selective Prediction in LLMs
Edward Phillips, Fredrik K. Gustafsson, Sean Wu +2
Selective prediction systems can mitigate harms resulting from language model hallucinations by abstaining from answering in high-risk cases. Uncertainty quantification techniques…
Geometric Uncertainty for Detecting and Correcting Hallucinations in LLMs
Edward Phillips, Sean Wu, Soheila Molaei +3
Large language models are known to hallucinate, generating linguistically plausible but incorrect answers to questions. Uncertainty quantification has been proposed as a strategy t…
Large Language Models in the Clinic: A Comprehensive Benchmark
Fenglin Liu, Zheng Li, Hongjian Zhou +16
The adoption of large language models (LLMs) to assist clinicians has attracted remarkable attention. Existing works mainly adopt the close-ended question-answering (QA) task with…