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Large language models (LLMs) have attracted significant attention for their exceptional abilities in various natural language processing tasks, but they suffer from hallucinations that will cause performance degradation.
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Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2015
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Are NLP models really able to solve simple math word problems?
Arkil Patel, Satwik Bhattamishra, and Navin Goyal · 2021
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Training language models to follow instructions with human feedback, 2022
Long Ouyang, Jeff Wu, Xu Jiang, Diogo Almeida, Carroll L. Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, John Schulman, Jacob Hilton, Fraser Kelton, Luke Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul Christiano, Jan Leike, and Ryan Lowe · 2022
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Can large language models be an alternative to human evaluations?
Cheng-Han Chiang and Hung-yi Lee · 2023
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Chain-of-verification reduces hallucination in large language models, 2023
Shehzaad Dhuliawala, Mojtaba Komeili, Jing Xu, Roberta Raileanu, Xian Li, Asli Celikyilmaz, and Jason Weston · 2023
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Towards revealing the mystery behind chain of thought: A theoretical perspective
Guhao Feng, Bohang Zhang, Yuntian Gu, Haotian Ye, Di He, and Liwei Wang · 2023
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RARR: Researching and revising what language models say, using language models
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Self-verification improves few-shot clinical information extraction
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Large language models are zero-shot time series forecasters
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Language models can solve computer tasks, 2023
Geunwoo Kim, Pierre Baldi, and Stephen McAleer · 2023
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Are emergent abilities in large language models just in-context learning?, 2023
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Cycles of thought: Measuring llm confidence through stable explanations, 2024
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Large language models cannot self-correct reasoning yet
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Large language models can self-correct with minimal effort
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