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20232025
most citedSample then Identify: A General Framework for Risk Control and Assessment in Multimodal Large Language Models

3 citations · 3 across the 8 of their papers we have counts for

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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…

cs.CL2024★ 3 cited

Sample then Identify: A General Framework for Risk Control and Assessment in Multimodal Large Language Models

Qingni Wang, Tiantian Geng, Zhiyuan Wang +3

Multimodal Large Language Models (MLLMs) exhibit promising advancements across various tasks, yet they still encounter significant trustworthiness issues. Prior studies apply Split…

cs.CL2024

ConU: Conformal Uncertainty in Large Language Models with Correctness Coverage Guarantees

Zhiyuan Wang, Jinhao Duan, Lu Cheng +6

Uncertainty quantification (UQ) in natural language generation (NLG) tasks remains an open challenge, exacerbated by the closed-source nature of the latest large language models (L…

cs.CL2024

Word-Sequence Entropy: Towards Uncertainty Estimation in Free-Form Medical Question Answering Applications and Beyond

Zhiyuan Wang, Jinhao Duan, Chenxi Yuan +6

Uncertainty estimation is crucial for the reliability of safety-critical human and artificial intelligence (AI) interaction systems, particularly in the domain of healthcare engine…