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As large language models (LLMs) continue to evolve, understanding and quantifying the uncertainty in their predictions is critical for enhancing application credibility.
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Takeshi Kojima, Shixiang (Shane) Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa. 2022 · 2022
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Luping Liu, Meiling Wang, Mozhi Zhang, Linbo Qing, and Xiaohai He. 2022 · 2022
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Out-of-distribution detection and selective generation for conditional language models
Jie Ren, Jiaming Luo, Yao Zhao, Kundan Krishna, Mohammad Saleh, Balaji Lakshminarayanan, and Peter J Liu. 2022 · 2022
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Haoyu Wang, Hongming Zhang, Yuqian Deng, Jacob R. Gardner, Dan Roth, and Muhao Chen. 2022 · 2022
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Uncertainty quantification with pre-trained language models: A large-scale empirical analysis
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Hussam Alkaissi and Samy I McFarlane. 2023 · 2023
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Quantifying uncertainty in answers from any language model and enhancing their trustworthiness
Jiuhai Chen and Jonas Mueller. 2023 · 2023
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Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc Le, Ed Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou. 2023 · 2023
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Quantifying aleatoric and epistemic uncertainty in machine learning: Are conditional entropy and mutual information appropriate measures?
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SAC 3 : Reliable hallucination detection in black-box language models via semantic-aware cross-check consistency
Jiaxin Zhang, Zhuohang Li, Kamalika Das, Bradley Malin, and Sricharan Kumar. 2023a · 2023
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Yu Zhu, Yingchun Ye, Mengyang Li, Ji Zhang, and Ou Wu. 2023 · 2023
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Hallucination detection in llms: Fast and memory-efficient finetuned models
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Uncertainty estimation in large language models to support biodiversity conservation
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Can llms express their uncertainty? an empirical evaluation of confidence elicitation in llms
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