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cs.CL2026
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…
cs.CL2025
Annotation-Efficient Universal Honesty Alignment
Shiyu Ni, Keping Bi, Jiafeng Guo +4
Honesty alignment-the ability of large language models (LLMs) to recognize their knowledge boundaries and express calibrated confidence-is essential for trustworthy deployment. Exi…
cs.CL2024
PEAR: Position-Embedding-Agnostic Attention Re-weighting Enhances Retrieval-Augmented Generation with Zero Inference Overhead
Tao Tan, Yining Qian, Ang Lv +7
Large language models (LLMs) enhanced with retrieval-augmented generation (RAG) have introduced a new paradigm for web search. However, the limited context awareness of LLMs degrad…