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cs.AI2025
SAFER: Risk-Constrained Sample-then-Filter in Large Language Models
Qingni Wang, Yue Fan, Xin Eric Wang
As large language models (LLMs) are increasingly deployed in risk-sensitive applications such as real-world open-ended question answering (QA), ensuring the trustworthiness of thei…
cs.CL2025
COIN: Uncertainty-Guarding Selective Question Answering for Foundation Models with Provable Risk Guarantees
Zhiyuan Wang, Jinhao Duan, Qingni Wang +4
Uncertainty quantification (UQ) for foundation models is essential to identify and mitigate potential hallucinations in automatically generated text. However, heuristic UQ approach…
cs.CL2025
SConU: Selective Conformal Uncertainty in Large Language Models
Zhiyuan Wang, Qingni Wang, Yue Zhang +4
As large language models are increasingly utilized in real-world applications, guarantees of task-specific metrics are essential for their reliable deployment. Previous studies hav…