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Large language models (LLMs) can generate fluent natural language texts when given relevant documents as background context.
The use of the area under the roc curve in the evaluation of machine learning algorithms
Andrew P Bradley · 1997
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Get to the point: Summarization with pointer-generator networks
Abigail See, Peter J Liu, and Christopher D Manning · 2017
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Don’t give me the details, just the summary! topic-aware convolutional neural networks for extreme summarization
Shashi Narayan, Shay B Cohen, and Mirella Lapata · 2018
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Ranking generated summaries by correctness: An interesting but challenging application for natural language inference
Tobias Falke, Leonardo FR Ribeiro, Prasetya Ajie Utama, Ido Dagan, and Iryna Gurevych · 2019
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Bertscore: Evaluating text generation with bert
Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q Weinberger, and Yoav Artzi · 2019
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On faithfulness and factuality in abstractive summarization
Joshua Maynez, Shashi Narayan, Bernd Bohnet, and Ryan McDonald · 2020
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Evaluating the factual consistency of abstractive text summarization
Wojciech Kryściński, Bryan McCann, Caiming Xiong, and Richard Socher · 2020
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Asking and answering questions to evaluate the factual consistency of summaries
Alex Wang, Kyunghyun Cho, and Mike Lewis · 2020
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Evaluating factuality in generation with dependency-level entailment
Tanya Goyal and Greg Durrett · 2020
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Optimizing the factual correctness of a summary: A study of summarizing radiology reports
Yuhao Zhang, Derek Merck, Emily Tsai, Christopher D Manning, and Curtis Langlotz · 2020
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Factual error correction for abstractive summarization models
Meng Cao, Yue Dong, Jiapeng Wu, and Jackie Chi Kit Cheung · 2020
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On faithfulness and factuality in abstractive summarization
Joshua Maynez, Shashi Narayan, Bernd Bohnet, and Ryan McDonald · 2020
Earlier work this paper cites.
Entity-level factual consistency of abstractive text summarization
Feng Nan, Ramesh Nallapati, Zhiguo Wang, Cicero dos Santos, Henghui Zhu, Dejiao Zhang, Kathleen Mckeown, and Bing Xiang · 2021
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Detecting hallucinated content in conditional neural sequence generation
Chunting Zhou, Graham Neubig, Jiatao Gu, Mona Diab, Francisco Guzmán, Luke Zettlemoyer, and Marjan Ghazvininejad · 2021
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Measuring attribution in natural language generation models
Hannah Rashkin, Vitaly Nikolaev, Matthew Lamm, Lora Aroyo, Michael Collins, Dipanjan Das, Slav Petrov, Gaurav Singh Tomar, Iulia Turc, and David Reitter · 2021
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Summeval: Re-evaluating summarization evaluation
Alexander Richard Fabbri, Wojciech Kryściński, Bryan McCann, Caiming Xiong, Richard Socher, and Dragomir Radev · 2021
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Palm: Scaling language modeling with pathways
Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, et al · 2022
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Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al · 2022
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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 Glass, and Pengcheng He · 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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Improving factuality and reasoning in language models through multiagent debate
Yilun Du, Shuang Li, Antonio Torralba, Joshua B Tenenbaum, and Igor Mordatch · 2023
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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 Zhao, Ni Lao, Hongrae Lee, Da-Cheng Juan, et al · 2023
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Helma: A large-scale hallucination evaluation benchmark for large language models
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Wenhu Chen, Xueguang Ma, Xinyi Wang, and William W Cohen · 2022
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Measuring and narrowing the compositionality gap in language models
Ofir Press, Muru Zhang, Sewon Min, Ludwig Schmidt, Noah A Smith, and Mike Lewis · 2022
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Faithful to the document or to the world? mitigating hallucinations via entity-linked knowledge in abstractive summarization
Yue Dong, John Wieting, and Pat Verga · 2022
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Llama: Open and efficient foundation language models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al · 2023
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OpenAI · 2023
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Lost in the middle: How language models use long contexts
Nelson F Liu, Kevin Lin, John Hewitt, Ashwin Paranjape, Michele Bevilacqua, Fabio Petroni, and Percy Liang · 2023
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Large language models can be easily distracted by irrelevant context
Freda Shi, Xinyun Chen, Kanishka Misra, Nathan Scales, David Dohan, Ed Huai hsin Chi, Nathanael Scharli, and Denny Zhou · 2023
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How language model hallucinations can snowball
Muru Zhang, Ofir Press, William Merrill, Alisa Liu, and Noah A Smith · 2023
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Junyi Li, Xiaoxue Cheng, Wayne Xin Zhao, Jian-Yun Nie, and Ji-Rong Wen · 2023
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Why does chatgpt fall short in providing truthful answers?
Kevin Chen-Chuan Chang Shen Zheng, Jie Huang · 2023
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Evaluating the logical reasoning ability of chatgpt and gpt-4
Hanmeng Liu, Ruoxi Ning, Zhiyang Teng, Jian Liu, Qiji Zhou, and Yue Zhang · 2023
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Gpteval: Nlg evaluation using gpt-4 with better human alignment
Yang Liu, Dan Iter, Yichong Xu, Shuohang Wang, Ruochen Xu, and Chenguang Zhu · 2023
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Sources of hallucination by large language models on inference tasks
Nick McKenna, Tianyi Li, Liang Cheng, Mohammad Javad Hosseini, Mark Johnson, and Mark Steedman · 2023
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A survey of hallucination in large foundation models
Vipula Rawte, Amit Sheth, and Amitava Das · 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
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Self-contradictory hallucinations of large language models: Evaluation, detection and mitigation
Niels Mündler, Jingxuan He, Slobodan Jenko, and Martin Vechev · 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
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