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Large language models (LLMs) have demonstrated remarkable capabilities across various domains, although their susceptibility to hallucination poses significant challenges for their deployment in critical areas such as healthcare.
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Med-HALT: Medical Domain Hallucination Test for Large Language Models. In Proceedings of the 27th Conference on Computational Natural Language Learning (CoNLL) , Jing Jiang, David Reitter, and Shumin Deng (Eds.). Association for Computational Linguistics, Singapore, 314–334
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Siren’s song in the AI ocean: a survey on hallucination in large language models
Yue Zhang, Yafu Li, Leyang Cui, Deng Cai, Lemao Liu, Tingchen Fu, Xinting Huang, Enbo Zhao, Yu Zhang, Yulong Chen, et al · 2023
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Why does chatgpt fall short in providing truthful answers
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In-Context Sharpness as Alerts: An Inner Representation Perspective for Hallucination Mitigation
Shiqi Chen, Miao Xiong, Junteng Liu, Zhengxuan Wu, Teng Xiao, Siyang Gao, and Junxian He. 2024 · 2024
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Mitigating large language model hallucinations via autonomous knowledge graph-based retrofitting. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 38. 18126–18134
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Inference-time intervention: Eliciting truthful answers from a language model
Kenneth Li, Oam Patel, Fernanda Viégas, Hanspeter Pfister, and Martin Wattenberg. 2024 · 2024
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Unifying large language models and knowledge graphs: A roadmap
Shirui Pan, Linhao Luo, Yufei Wang, Chen Chen, Jiapu Wang, and Xindong Wu. 2024 · 2024
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A comprehensive survey of hallucination mitigation techniques in large language models
SM Tonmoy, SM Zaman, Vinija Jain, Anku Rani, Vipula Rawte, Aman Chadha, and Amitava Das. 2024 · 2024
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