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The surge in applications of large language models (LLMs) has prompted concerns about the generation of misleading or fabricated information, known as hallucinations.
Robust estimation of a location parameter
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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
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
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Coqa: A conversational question answering challenge
Siva Reddy, Danqi Chen, and Christopher D Manning · 2019
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Sticking to the facts: Confident decoding for faithful data-to-text generation
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Tydi qa: A benchmark for information-seeking question answering in ty pologically di verse languages
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Thibault Sellam, Dipanjan Das, and Ankur P Parikh · 2020
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Uncertainty estimation in autoregressive structured prediction
Andrey Malinin and Mark Gales · 2021
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Looking for a needle in a haystack: A comprehensive study of hallucinations in neural machine translation
Nuno M Guerreiro, Elena Voita, and André FT Martins · 2022
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Language models (mostly) know what they know
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Factuality enhanced language models for open-ended text generation
Nayeon Lee, Wei Ping, Peng Xu, Mostofa Patwary, Pascale N Fung, Mohammad Shoeybi, and Bryan Catanzaro · 2022
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Teaching models to express their uncertainty in words
Stephanie Lin, Jacob Hilton, and Owain Evans · 2022
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Truthfulqa: Measuring how models mimic human falsehoods
Stephanie Lin, Jacob Hilton, and Owain Evans · 2022
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Opt: Open pre-trained transformer language models
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The internal state of an llm knows when its lying
Amos Azaria and Tom Mitchell · 2023
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Feed two birds with one scone: Exploiting wild data for both out-of-distribution generalization and detection
Haoyue Bai, Gregory Canal, Xuefeng Du, Jeongyeol Kwon, Robert D Nowak, and Yixuan Li · 2023
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Discovering latent knowledge in language models without supervision
Collin Burns, Haotian Ye, Dan Klein, and Jacob Steinhardt · 2023
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Lm vs lm: Detecting factual errors via cross examination
Roi Cohen, May Hamri, Mor Geva, and Amir Globerson · 2023
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Shifting attention to relevance: Towards the uncertainty estimation of large language models
Jinhao Duan, Hao Cheng, Shiqi Wang, Chenan Wang, Alex Zavalny, Renjing Xu, Bhavya Kailkhura, and Kaidi Xu · 2023
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Look before you leap: An exploratory study of uncertainty measurement for large language models
Yuheng Huang, Jiayang Song, Zhijie Wang, Huaming Chen, and Lei Ma · 2023
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Survey of hallucination in natural language generation
Hallucination detection for generative large language models by bayesian sequential estimation
Xiaohua Wang, Yuliang Yan, Longtao Huang, Xiaoqing Zheng, and Xuan-Jing Huang · 2023
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Enhancing uncertainty-based hallucination detection with stronger focus
Tianhang Zhang, Lin Qiu, Qipeng Guo, Cheng Deng, Yue Zhang, Zheng Zhang, Chenghu Zhou, Xinbing Wang, and Luoyi Fu · 2023
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Alleviating hallucinations of large language models through induced hallucinations
Yue Zhang, Leyang Cui, Wei Bi, and Shuming Shi · 2023
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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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Navigating the grey area: Expressions of overconfidence and uncertainty in language models
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Ziwei Ji, Nayeon Lee, Rita Frieske, Tiezheng Yu, Dan Su, Yan Xu, Etsuko Ishii, Ye Jin Bang, Andrea Madotto, and Pascale Fung · 2023
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Semantic uncertainty: Linguistic invariances for uncertainty estimation in natural language generation
Lorenz Kuhn, Yarin Gal, and Sebastian Farquhar · 2023
Cited alongside, same era.
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
Cited alongside, same era.
Inference-time intervention: Eliciting truthful answers from a language model
Kenneth Li, Oam Patel, Fernanda Viégas, Hanspeter Pfister, and Martin Wattenberg · 2023
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Generating with confidence: Uncertainty quantification for black-box large language models
Zhen Lin, Shubhendu Trivedi, and Jimeng Sun · 2023
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Selfcheckgpt: Zero-resource black-box hallucination detection for generative large language models
Potsawee Manakul, Adian Liusie, and Mark JF Gales · 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 Wei Koh, Mohit Iyyer, Luke Zettlemoyer, and Hannaneh Hajishirzi · 2023
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Gpt-4 technical report, 2023
OpenAI · 2023
Cited alongside, same era.
Kaitlyn Zhou, Dan Jurafsky, and Tatsunori Hashimoto · 2023
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Representation engineering: A top-down approach to ai transparency
Andy Zou, Long Phan, Sarah Chen, James Campbell, Phillip Guo, Richard Ren, Alexander Pan, Xuwang Yin, Mantas Mazeika, Ann-Kathrin Dombrowski, et al · 2023
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Do language models know when they’re hallucinating references?
Ayush Agrawal, Lester Mackey, and Adam Tauman Kalai · 2024
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Out-of-distribution learning with human feedback
Haoyue Bai, Xuefeng Du, Katie Rainey, Shibin Parameswaran, and Yixuan Li · 2024
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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 · 2024
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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 · 2024
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How does unlabeled data provably help out-of-distribution detection?
Xuefeng Du, Zhen Fang, Ilias Diakonikolas, and Yixuan Li · 2024
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Do llms know about hallucination? an empirical investigation of llm’s hidden states
Hanyu Duan, Yi Yang, and Kar Yan Tam · 2024
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Sh2: Self-highlighted hesitation helps you decode more truthfully
Jushi Kai, Tianhang Zhang, Hai Hu, and Zhouhan Lin · 2024
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Self-contradictory hallucinations of large language models: Evaluation, detection and mitigation
Niels Mündler, Jingxuan He, Slobodan Jenko, and Martin Vechev · 2024
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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
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Can LLMs express their uncertainty? an empirical evaluation of confidence elicitation in LLMs
Miao Xiong, Zhiyuan Hu, Xinyang Lu, YIFEI LI, Jie Fu, Junxian He, and Bryan Hooi · 2024
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Hallucination is inevitable: An innate limitation of large language models
Ziwei Xu, Sanjay Jain, and Mohan Kankanhalli · 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
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