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Large language models (LMs) are prone to generate factual errors, which are often called hallucinations.
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Automated fact checking: Task formulations, methods and future directions
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A review on fact extraction and verification
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Entity-based knowledge conflicts in question answering
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Automated fact-checking for assisting human fact-checkers
Preslav Nakov, David Corney, Maram Hasanain, Firoj Alam, Tamer Elsayed, Alberto Barrón-Cedeño, Paolo Papotti, Shaden Shaar, and Giovanni Da San Martino · 2021
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Understanding factuality in abstractive summarization with FRANK: A benchmark for factuality metrics
Artidoro Pagnoni, Vidhisha Balachandran, and Yulia Tsvetkov · 2021
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Get your vitamin C! robust fact verification with contrastive evidence
Tal Schuster, Adam Fisch, and Regina Barzilay · 2021
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Correcting diverse factual errors in abstractive summarization via post-editing and language model infilling
Vidhisha Balachandran, Hannaneh Hajishirzi, William Cohen, and Yulia Tsvetkov · 2022
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Evaluating factuality in text simplification
Ashwin Devaraj, William Sheffield, Byron Wallace, and Junyi Jessy Li · 2022
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When not to trust language models: Investigating effectiveness and limitations of parametric and non-parametric memories
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FActScore: Fine-grained atomic evaluation of factual precision in long form text generation
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No robots, 2023
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I Chern, Steffi Chern, Shiqi Chen, Weizhe Yuan, Kehua Feng, Chunting Zhou, Junxian He, Graham Neubig, Pengfei Liu, et al · 2023
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FactKB: Generalizable factuality evaluation using language models enhanced with factual knowledge
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RARR: Researching and revising what language models say, using language models
Luyu Gao, Zhuyun Dai, Panupong Pasupat, Anthony Chen, Arun Tejasvi Chaganty, Yicheng Fan, Vincent Y Zhao, Ni Lao, Hongrae Lee, Da-Cheng Juan, et al · 2023
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Survey of hallucination in natural language generation
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Openassistant conversations–democratizing large language model alignment
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Siren’s song in the ai ocean: A survey on hallucination in large language models
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