3 papers
cs.AI2026
ConfidenceBench: Evaluating Confidence Calibration in Large Language Models
Matthew ffrench-Constant, Daniel Yang, Xinmeng Huang +1
Large language models (LLMs) are increasingly deployed in settings where fluent but incorrect answers can be costly. In these settings, accuracy alone is insufficient: models must…
cs.AI2024
One-Shot Safety Alignment for Large Language Models via Optimal Dualization
Xinmeng Huang, Shuo Li, Edgar Dobriban +3
The growing safety concerns surrounding large language models raise an urgent need to align them with diverse human preferences to simultaneously enhance their helpfulness and safe…
cs.CL2024
Uncertainty in Language Models: Assessment through Rank-Calibration
Xinmeng Huang, Shuo Li, Mengxin Yu +5
Language Models (LMs) have shown promising performance in natural language generation. However, as LMs often generate incorrect or hallucinated responses, it is crucial to correctl…