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Large Language Models (LLMs) often exhibit \textit{hallucinations}, generating factually incorrect or semantically irrelevant content in response to prompts.
Teaching machines to read and comprehend
Karl Moritz Hermann, Tomas Kocisky, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, and Phil Blunsom. 2015 · 2015
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TriviaQA: A large scale distantly supervised challenge dataset for reading comprehension
Mandar Joshi, Eunsol Choi, Daniel Weld, and Luke Zettlemoyer. 2017 · 2017
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Program induction by rationale generation: Learning to solve and explain algebraic word problems
Wang Ling, Dani Yogatama, Chris Dyer, and Phil Blunsom. 2017 · 2017
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A systematic classification of knowledge, reasoning, and context within the ARC dataset
Michael Boratko, Harshit Padigela, Divyendra Mikkilineni, Pritish Yuvraj, Rajarshi Das, Andrew McCallum, Maria Chang, Achille Fokoue-Nkoutche, Pavan Kapanipathi, Nicholas Mattei, Ryan Musa, Kartik Talamadupula, and Michael Witbrock. 2018 · 2018
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CommonsenseQA: A question answering challenge targeting commonsense knowledge
Alon Talmor, Jonathan Herzig, Nicholas Lourie, and Jonathan Berant. 2019 · 2019
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Measuring massive multitask language understanding
Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt. 2021 · 2021
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LoRA: Low-rank adaptation of large language models
Edward J Hu, yelong shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. 2022 · 2022
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Show your work: Scratchpads for intermediate computation with language models
Maxwell Nye, Anders Johan Andreassen, Guy Gur-Ari, Henryk Michalewski, Jacob Austin, David Bieber, David Dohan, Aitor Lewkowycz, Maarten Bosma, David Luan, Charles Sutton, and Augustus Odena. 2022 · 2022
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Chain of thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, brian ichter, Fei Xia, Ed H. Chi, Quoc V Le, and Denny Zhou. 2022 · 2022
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The internal state of an LLM knows when it’s lying
Amos Azaria and Tom Mitchell. 2023 · 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 · 2023
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Survey of hallucination in natural language generation
Ziwei Ji, Nayeon Lee, Rita Frieske, Tiezheng Yu, Dan Su, Yan Xu, Etsuko Ishii, Ye Jin Bang, Andrea Madotto, and Pascale Fung. 2023 · 2023
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Albert Q Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, et al. 2023 · 2023
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Language models can solve computer tasks
Geunwoo Kim, Pierre Baldi, and Stephen McAleer. 2023 · 2023
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HaluEval: A large-scale hallucination evaluation benchmark for large language models
Junyi Li, Xiaoxue Cheng, Xin Zhao, Jian-Yun Nie, and Ji-Rong Wen. 2023a · 2023
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Federated prompting and chain-of-thought reasoning for improving llms answering
Xiangyang Liu, Tianqi Pang, and Chenyou Fan. 2023 · 2023
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When not to trust language models: Investigating effectiveness of parametric and non-parametric memories
Alex Mallen, Akari Asai, Victor Zhong, Rajarshi Das, Daniel Khashabi, and Hannaneh Hajishirzi. 2023 · 2023
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SelfcheckGPT: Zero-resource black-box hallucination detection for generative large language models
Potsawee Manakul, Adian Liusie, and Mark Gales. 2023 · 2023
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FActScore: Fine-grained atomic evaluation of factual precision in long form text generation
Sewon Min, Kalpesh Krishna, Xinxi Lyu, Mike Lewis, Wen-tau Yih, Pang Koh, Mohit Iyyer, Luke Zettlemoyer, and Hannaneh Hajishirzi. 2023 · 2023
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Why think step by step? reasoning emerges from the locality of experience
Ben Prystawski, Michael Y. Li, and Noah Goodman. 2023 · 2023
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Language models are greedy reasoners: A systematic formal analysis of chain-of-thought
Abulhair Saparov and He He. 2023 · 2023
Cited alongside, same era.
Language models don’t always say what they think: Unfaithful explanations in chain-of-thought prompting
Miles Turpin, Julian Michael, Ethan Perez, and Samuel R. Bowman. 2023 · 2023
Cited alongside, same era.
Self-consistency improves chain of thought reasoning in language models
Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc Le, Ed Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou. 2023 · 2023
Cited alongside, same era.
Harnessing the power of llms in practice: A survey on chatgpt and beyond
Jingfeng Yang, Hongye Jin, Ruixiang Tang, Xiaotian Han, Qizhang Feng, Haoming Jiang, Bing Yin, and Xia Hu. 2023 · 2023
Cited alongside, same era.
Tree of thoughts: Deliberate problem solving with large language models
Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Thomas L. Griffiths, Yuan Cao, and Karthik R Narasimhan. 2023 · 2023
Detecting hallucinations in large language model generation: A token probability approach
Ernesto Quevedo, Jorge Yero, Rachel Koerner, Pablo Rivas, and Tomas Cerny. 2024 · 2024
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Provenance: A light-weight fact-checker for retrieval augmented LLM generation output
Hithesh Sankararaman, Mohammed Nasheed Yasin, Tanner Sorensen, Alessandro Di Bari, and Andreas Stolcke. 2024 · 2024
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MuSR: Testing the limits of chain-of-thought with multistep soft reasoning
Zayne Rea Sprague, Xi Ye, Kaj Bostrom, Swarat Chaudhuri, and Greg Durrett. 2024 · 2024
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LLM-check: Investigating detection of hallucinations in large language models
Gaurang Sriramanan, Siddhant Bharti, Vinu Sankar Sadasivan, Shoumik Saha, Priyatham Kattakinda, and Soheil Feizi. 2024 · 2024
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Chain of thoughtlessness? an analysis of cot in planning
Kaya Stechly, Karthik Valmeekam, and Subbarao Kambhampati. 2024 · 2024
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Cited alongside, same era.
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, Longyue Wang, Anh Tuan Luu, Wei Bi, Freda Shi, and Shuming Shi. 2023 · 2023
Cited alongside, same era.
Least-to-most prompting enables complex reasoning in large language models
Denny Zhou, Nathanael Schärli, Le Hou, Jason Wei, Nathan Scales, Xuezhi Wang, Dale Schuurmans, Claire Cui, Olivier Bousquet, Quoc V Le, and Ed H. Chi. 2023 · 2023
Cited alongside, same era.
Dola: Decoding by contrasting layers improves factuality in large language models
Yung-Sung Chuang, Yujia Xie, Hongyin Luo, Yoon Kim, James R. Glass, and Pengcheng He. 2024 · 2024
Cited alongside, same era.
Active prompting with chain-of-thought for large language models
Shizhe Diao, Pengcheng Wang, Yong Lin, Rui Pan, Xiang Liu, and Tong Zhang. 2024 · 2024
Cited alongside, same era.
Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Amy Yang, Angela Fan, et al. 2024 · 2024
Cited alongside, same era.
Large language models: a comprehensive survey of its applications, challenges, limitations, and future prospects
Muhammad Usman Hadi, Qasem Al Tashi, Abbas Shah, Rizwan Qureshi, Amgad Muneer, Muhammad Irfan, Anas Zafar, Muhammad Bilal Shaikh, Naveed Akhtar, Jia Wu, et al. 2024 · 2024
Cited alongside, same era.
A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions
Lei Huang, Weijiang Yu, Weitao Ma, Weihong Zhong, Zhangyin Feng, Haotian Wang, Qianglong Chen, Weihua Peng, Xiaocheng Feng, Bing Qin, and Ting Liu. 2024 · 2024
Cited alongside, same era.
Unsupervised real-time hallucination detection based on the internal states of large language models
Weihang Su, Changyue Wang, Qingyao Ai, Yiran Hu, Zhijing Wu, Yujia Zhou, and Yiqun Liu. 2024 · 2024
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A comprehensive survey of hallucination mitigation techniques in large language models
S. M Towhidul Islam Tonmoy, S M Mehedi Zaman, Vinija Jain, Anku Rani, Vipula Rawte, Aman Chadha, and Amitava Das. 2024 · 2024
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Does usage scenario matter? investigating user perceptions, attitude and support for policies towards chatgpt
Wenjia Yan, Bo Hu, Yu li Liu, Changyan Li, and Chuling Song. 2024 · 2024
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GoT: Effective graph-of-thought reasoning in language models
Yao Yao, Zuchao Li, and Hai Zhao. 2024 · 2024
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Large language models as analogical reasoners
Michihiro Yasunaga, Xinyun Chen, Yujia Li, Panupong Pasupat, Jure Leskovec, Percy Liang, Ed H. Chi, and Denny Zhou. 2024 · 2024
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How language model hallucinations can snowball
Muru Zhang, Ofir Press, William Merrill, Alisa Liu, and Noah A. Smith. 2024 · 2024
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A survey of large language models
Wayne Xin Zhao, Kun Zhou, Junyi Li, Tianyi Tang, Xiaolei Wang, Yupeng Hou, Yingqian Min, Beichen Zhang, Junjie Zhang, Zican Dong, Yifan Du, Chen Yang, Yushuo Chen, Zhipeng Chen, Jinhao Jiang, Ruiyang Ren, Yifan Li, Xinyu Tang, Zikang Liu, Peiyu Liu, Jian-Yun Nie, and Ji-Rong Wen. 2024 · 2024
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Enhancing uncertainty modeling with semantic graph for hallucination detection
Kedi Chen, Qin Chen, Jie Zhou, Xinqi Tao, Bowen Ding, Jingwen Xie, Mingchen Xie, Peilong Li, and Zheng Feng. 2025 · 2025
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Knowledge updating? no more model editing! just selective contextual reasoning
Guoxiu He, Xin Song, and Aixin Sun. 2025 · 2025
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Text clustering as classification with llms
Chen Huang and Guoxiu He. 2025 · 2025
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Retrieve–revise–refine: A novel framework for retrieval of concise entailing legal article set
Chau Nguyen, Phuong Nguyen, and Le-Minh Nguyen. 2025 · 2025
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Divide and Conquer: Prompting Large Language Models to Identify Personalities in Long Social Posts via Chunked Voting
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Interweaving memories of a siamese large language model
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Silent LLMs: Using LoRA to Enable LLMs to Identify Hate Speech
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Metacognitive symbolic distillation framework for multi-choice machine reading comprehension
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