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cs.CL2026
Confidence Should Be Calibrated More Than One Turn Deep
Zhaohan Zhang, Chengzhengxu Li, Xiaoming Liu +3
Large Language Models (LLMs) are increasingly applied in high-stakes domains such as finance, healthcare, and education, where reliable multi-turn interactions with users are essen…
cs.CL2025
GrACE: A Generative Approach to Better Confidence Elicitation and Efficient Test-Time Scaling in Large Language Models
Zhaohan Zhang, Ziquan Liu, Ioannis Patras
Assessing the reliability of Large Language Models (LLMs) by confidence elicitation is a prominent approach to AI safety in high-stakes applications, such as healthcare and finance…
cs.CL2024
Get Confused Cautiously: Textual Sequence Memorization Erasure with Selective Entropy Maximization
Zhaohan Zhang, Ziquan Liu, Ioannis Patras
Large Language Models (LLMs) have been found to memorize and recite some of the textual sequences from their training set verbatim, raising broad concerns about privacy and copyrig…