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cs.CL2026
When and What to Ask: AskBench and Rubric-Guided RLVR for LLM Clarification
Jiale Zhao, Ke Fang, Lu Cheng
Large language models (LLMs) often respond even when prompts omit critical details or include misleading information, leading to hallucinations or reinforced misconceptions. We stu…
cs.CL2025
REHEARSE: Experiential Rehearsal for Verbal Confidence Calibration in Large Language Models
Ke Fang, Tianyi Zhao, Qianwen Wang +1
Large language models (LLMs) often express verbal confidence that is poorly aligned with actual correctness, limiting their reliability in safety-critical applications. Existing pr…
cs.CL2024
TurtleBench: Evaluating Top Language Models via Real-World Yes/No Puzzles
Qingchen Yu, Shichao Song, Ke Fang +5
As the application of Large Language Models (LLMs) expands, the demand for reliable evaluations increases. Existing LLM evaluation benchmarks primarily rely on static datasets, mak…