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Large Language Models (LLMs) are increasingly used in various contexts, yet remain prone to generating non-factual content, commonly referred to as "hallucinations".
Interrater reliability: The kappa statistic
Mary McHugh. 2012 · 2012
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Diving deep into modes of fact hallucinations in dialogue systems
Souvik Das, Sougata Saha, and Rohini Srihari. 2022 · 2022
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Rohan Anil, Andrew M. Dai, Orhan Firat, Melvin Johnson, and Dmitry Lepikhin et al. 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, and Devendra Singh Chaplot et al. 2023 · 2023
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Evaluating open-domain question answering in the era of large language models
Ehsan Kamalloo, Nouha Dziri, Charles Clarke, and Davood Rafiei. 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. 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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Sources of hallucination by large language models on inference tasks
Nick McKenna, Tianyi Li, Liang Cheng, Mohammad Hosseini, Mark Johnson, and Mark Steedman. 2023 · 2023
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Neeraj Varshney, Wenlin Yao, Hongming Zhang, Jianshu Chen, and Dong Yu. 2023 · 2023
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Wikidata as a source of demographic information
Samir Abdaljalil and Hamdy Mubarak. 2024 · 2024
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Llms will always hallucinate, and we need to live with this
Sourav Banerjee, Ayushi Agarwal, and Saloni Singla. 2024 · 2024
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On learning to summarize with large language models as references
Yixin Liu, Kejian Shi, Katherine He, Longtian Ye, Alexander Fabbri, Pengfei Liu, Dragomir Radev, and Arman Cohan. 2024 · 2024
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Fine-grained hallucination detection and editing for language models
Abhika Mishra, Akari Asai, Vidhisha Balachandran, Yizhong Wang, Graham Neubig, Yulia Tsvetkov, and Hannaneh Hajishirzi. 2024 · 2024
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Halwasa: Quantify and analyze hallucinations in large language models: Arabic as a case study
Hamdy Mubarak, Hend Al-Khalifa, and Khaloud Suliman Alkhalefah. 2024 · 2024
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OpenAI, Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, and et al. 2024 · 2024
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Analyzing LLM behavior in dialogue summarization: Unveiling circumstantial hallucination trends
Sanjana Ramprasad, Elisa Ferracane, and Zachary Lipton. 2024 · 2024
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Elijah Berberette, Jack Hutchins, and Amir Sadovnik. 2024 · 2024
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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
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ANAH: Analytical annotation of hallucinations in large language models
Ziwei Ji, Yuzhe Gu, Wenwei Zhang, Chengqi Lyu, Dahua Lin, and Kai Chen. 2024 · 2024
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The dawn after the dark: An empirical study on factuality hallucination in large language models
Junyi Li, Jie Chen, Ruiyang Ren, Xiaoxue Cheng, Xin Zhao, Jian-Yun Nie, and Ji-Rong Wen. 2024 · 2024
Cited alongside, same era.
INSIDE: LLMs’ internal states retain the power of hallucination detection
Chao Chen, Kai Liu, Ze Chen, Yi Gu, Yue Wu, Mingyuan Tao, Zhihang Fu, and Jieping Ye. 2024a
Cited in the paper.
Unified hallucination detection for multimodal large language models
Xiang Chen, Chenxi Wang, Yida Xue, Ningyu Zhang, Xiaoyan Yang, Qiang Li, Yue Shen, Lei Liang, Jinjie Gu, and Huajun Chen. 2024b
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Gemini Team, Petko Georgiev, Ving Ian Lei, Ryan Burnell, and Libin Bai et al. 2024 · 2024
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Multilingual machine translation with large language models: Empirical results and analysis
Wenhao Zhu, Hongyi Liu, Qingxiu Dong, Jingjing Xu, Shujian Huang, Lingpeng Kong, Jiajun Chen, and Lei Li. 2024 · 2024
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An Yang, Baosong Yang, Beichen Zhang, Binyuan Hui, and Bo Zheng et al. 2025 · 2025
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