2 citations · 6 across the 14 of their papers we have counts for
4 papers · 1 filter
Shared Experience, Separate Learning: Companion Confidence Calibration for LLMs
Shiyu Ni, Keping Bi, Jiafeng Guo +4
Reliable self-assessment is essential for large language models (LLMs), yet they often remain highly confident when their answers are wrong. We study \emph{concurrent confidence ca…
Can LLM Rerankers Predict Their Own Ranking Performance?
Shiyu Ni, Keping Bi, Jiafeng Guo +3
Retrieval effectiveness varies substantially across queries, making it important to estimate ranking quality before relevance judgments are available. Query performance prediction…
How Long Reasoning Chains Influence LLMs' Judgment of Answer Factuality
Minzhu Tu, Shiyu Ni, Keping Bi
Large language models (LLMs) has been widely adopted as a scalable surrogate for human evaluation, yet such judges remain imperfect and susceptible to surface-level biases. One pos…
Evaluating and Calibrating LLM Confidence on Questions with Multiple Correct Answers
Yuhan Wang, Shiyu Ni, Zhikai Ding +3
Confidence calibration is essential for making large language models (LLMs) reliable, yet existing training-free methods have been primarily studied under single-answer question an…