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Large language models (LLMs) are susceptible to hallucinations -- factually incorrect outputs -- leading to a large body of work on detecting and mitigating such cases.
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, and 1 others. 2020 · 1901
Earlier work this paper cites.
Scikit-learn: Machine learning in python
Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, and 1 others. 2011 · 2011
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Triviaqa: A large scale distantly supervised challenge dataset for reading comprehension
Mandar Joshi, Eunsol Choi, Daniel S Weld, and Luke Zettlemoyer. 2017 · 2017
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Natural questions: a benchmark for question answering research
Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, Michael Collins, Ankur Parikh, Chris Alberti, Danielle Epstein, Illia Polosukhin, Jacob Devlin, Kenton Lee, and 1 others. 2019 · 2019
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Language models as knowledge bases?
Fabio Petroni, Tim Rocktäschel, Sebastian Riedel, Patrick Lewis, Anton Bakhtin, Yuxiang Wu, and Alexander Miller. 2019 · 2019
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How can we know what language models know?
Zhengbao Jiang, Frank F. Xu, Jun Araki, and Graham Neubig. 2020 · 2020
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Measuring and improving consistency in pretrained language models
Yanai Elazar, Nora Kassner, Shauli Ravfogel, Abhilasha Ravichander, Ed Hovy, Hinrich Schutze, and Yoav Goldberg. 2021 · 2021
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Discovering latent knowledge in language models without supervision
Collin Burns, Haotian Ye, Dan Klein, and Jacob Steinhardt. 2022 · 2022
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Language models (mostly) know what they know
Saurav Kadavath, Tom Conerly, Amanda Askell, Tom Henighan, Dawn Drain, Ethan Perez, Nicholas Schiefer, Zac Hatfield-Dodds, Nova DasSarma, Eli Tran-Johnson, and 1 others. 2022 · 2022
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Introducing chatgpt
OpenAI. 2022 · 2022
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Prompting gpt-3 to be reliable
Chenglei Si, Zhe Gan, Zhengyuan Yang, Shuohang Wang, Jianfeng Wang, Jordan Lee Boyd-Graber, and Lijuan Wang. 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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Autohall: Automated hallucination dataset generation for large language models
Zouying Cao, Yifei Yang, and Hai Zhao. 2023 · 2023
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Do androids know they’re only dreaming of electric sheep?
Sky CH-Wang, Benjamin Van Durme, Jason Eisner, and Chris Kedzie. 2023 · 2023
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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. 2023 · 2023
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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. 2023 · 2023
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Chain-of-verification reduces hallucination in large language models
Shehzaad Dhuliawala, Mojtaba Komeili, Jing Xu, Roberta Raileanu, Xian Li, Asli Celikyilmaz, and Jason Weston. 2023 · 2023
Earlier work this paper cites.
Llm factoscope: Uncovering llms’ factual discernment through intermediate data analysis
Jinwen He, Yujia Gong, Kai Chen, Zijin Lin, Chengan Wei, and Yue Zhao. 2023 · 2023
Earlier work this paper cites.
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
Cited alongside, same era.
Albert Q Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, and 1 others. 2023 · 2023
Cited alongside, same era.
Personas as a way to model truthfulness in language models
Nitish Joshi, Javier Rando, Abulhair Saparov, Najoung Kim, and He He. 2023 · 2023
Cited alongside, same era.
Calibrated language models must hallucinate
Adam Tauman Kalai and Santosh S Vempala. 2023 · 2023
Cited alongside, same era.
Inference-time intervention: Eliciting truthful answers from a language model
How language model hallucinations can snowball
Muru Zhang, Ofir Press, William Merrill, Alisa Liu, and Noah A Smith. 2023 · 2023
Later among the works it cites.
Truth forest: Toward multi-scale truthfulness in large language models through intervention without tuning
Zhongzhi Chen, Xingwu Sun, Xianfeng Jiao, Fengzong Lian, Zhanhui Kang, Di Wang, and Chengzhong Xu. 2024 · 2024
Closest in time.
Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Amy Yang, Angela Fan, and 1 others. 2024 · 2024
Closest in time.
Don’t hallucinate, abstain: Identifying llm knowledge gaps via multi-llm collaboration
Shangbin Feng, Weijia Shi, Yike Wang, Wenxuan Ding, Vidhisha Balachandran, and Yulia Tsvetkov. 2024 · 2024
Closest in time.
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Kenneth Li, Oam Patel, Fernanda Viégas, Hanspeter Pfister, and Martin Wattenberg. 2023 · 2023
Cited alongside, same era.
Selfcheckgpt: Zero-resource black-box hallucination detection for generative large language models
Potsawee Manakul, Adian Liusie, and Mark Gales. 2023 · 2023
Cited alongside, same era.
Samuel Marks and Max Tegmark. 2023 · 2023
Cited alongside, same era.
The waluigi effect (mega-post)
Cleo Nardo. 2023 · 2023
Cited alongside, same era.
How to catch an ai liar: Lie detection in black-box llms by asking unrelated questions
Lorenzo Pacchiardi, Alex James Chan, Sören Mindermann, Ilan Moscovitz, Alexa Yue Pan, Yarin Gal, Owain Evans, and Jan M Brauner. 2023 · 2023
Cited alongside, same era.
Weakly supervised detection of hallucinations in llm activations
Miriam Rateike, Celia Cintas, John Wamburu, Tanya Leah Akumu, and Skyler Speakman. 2023 · 2023
Cited alongside, same era.
Evgeniia Razumovskaia, Ivan Vulić, Pavle Marković, Tomasz Cichy, Qian Zheng, Tsung-Hsien Wen, and Paweł Budzianowski. 2023 · 2023
Cited alongside, same era.
Investigating the factual knowledge boundary of large language models with retrieval augmentation
Ruiyang Ren, Yuhao Wang, Yingqi Qu, Wayne Xin Zhao, Jing Liu, Hao Tian, Hua Wu, Ji-Rong Wen, and Haifeng Wang. 2023 · 2023
Cited alongside, same era.
Tao Ge, Xin Chan, Xiaoyang Wang, Dian Yu, Haitao Mi, and Dong Yu. 2024 · 2024
Closest in time.
Does fine-tuning llms on new knowledge encourage hallucinations?
Zorik Gekhman, Gal Yona, Roee Aharoni, Matan Eyal, Amir Feder, Roi Reichart, and Jonathan Herzig. 2024 · 2024
Closest in time.
Nl-iti: Optimizing probing and intervention for improvement of iti method
Jakub Hoscilowicz, Adam Wiacek, Jan Chojnacki, Adam Cieslak, Leszek Michon, Vitalii Urbanevych, and Artur Janicki. 2024 · 2024
Closest in time.
Llm internal states reveal hallucination risk faced with a query
Ziwei Ji, Delong Chen, Etsuko Ishii, Samuel Cahyawijaya, Yejin Bang, Bryan Wilie, and Pascale Fung. 2024 · 2024
Closest in time.
On large language models’ hallucination with regard to known facts
Che Jiang, Biqing Qi, Xiangyu Hong, Dayuan Fu, Yang Cheng, Fandong Meng, Mo Yu, Bowen Zhou, and Jie Zhou. 2024 · 2024
Closest in time.
Still no lie detector for language models: Probing empirical and conceptual roadblocks
Benjamin A Levinstein and Daniel A Herrmann. 2024 · 2024
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Frontier models are capable of in-context scheming
Alexander Meinke, Bronson Schoen, Jérémy Scheurer, Mikita Balesni, Rusheb Shah, and Marius Hobbhahn. 2024 · 2024
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Null-shot prompting: Rethinking prompting large language models with hallucination
Pittawat Taveekitworachai, Febri Abdullah, and Ruck Thawonmas. 2024 · 2024
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Gemma 2: Improving open language models at a practical size
Gemma Team, Morgane Riviere, Shreya Pathak, Pier Giuseppe Sessa, Cassidy Hardin, Surya Bhupatiraju, Léonard Hussenot, Thomas Mesnard, Bobak Shahriari, Alexandre Ramé, and 1 others. 2024 · 2024
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Zhihua Wen, Zhiliang Tian, Zexin Jian, Zhen Huang, Pei Ke, Yifu Gao, Minlie Huang, and Dongsheng Li. 2024 · 2024
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Characterizing truthfulness in large language model generations with local intrinsic dimension
Fan Yin, Jayanth Srinivasa, and Kai-Wei Chang. 2024 · 2024
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Yi Zeng, Hongpeng Lin, Jingwen Zhang, Diyi Yang, Ruoxi Jia, and Weiyan Shi. 2024 · 2024
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Exploring knowledge boundaries in large language models for retrieval judgment
Zhen Zhang, Xinyu Wang, Yong Jiang, Zhuo Chen, Feiteng Mu, Mengting Hu, Pengjun Xie, and Fei Huang. 2024b · 2024
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TrueTeacher: Learning factual consistency evaluation with large language models
Zorik Gekhman, Jonathan Herzig, Roee Aharoni, Chen Elkind, and Idan Szpektor. 2023 · 2070
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