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With the recent appearance of LLMs in practical settings, having methods that can effectively detect factual inconsistencies is crucial to reduce the propagation of misinformation and improve trust in model outputs.
Language models are few-shot learners
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On faithfulness and factuality in abstractive summarization
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Legal-bert: The muppets straight out of law school
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char-rnn
Andrej Karpathy. 2015 · 2015
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Shashi Narayan, Shay B. Cohen, and Mirella Lapata. 2018 · 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 · 2019
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Samsum corpus: A human-annotated dialogue dataset for abstractive summarization
Bogdan Gliwa, Iwona Mochol, Maciej Biesek, and Aleksander Wawer. 2019 · 2019
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Billsum: A corpus for automatic summarization of us legislation
Anastassia Kornilova and Vladimir Eidelman. 2019 · 2019
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Neural text summarization: A critical evaluation
Wojciech Kryściński, Nitish Shirish Keskar, Bryan McCann, Caiming Xiong, and Richard Socher. 2019 · 2019
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The impact of class imbalance in classification performance metrics based on the binary confusion matrix
Amalia Luque, Alejandro Carrasco, Alejandro Martín, and Ana de Las Heras. 2019 · 2019
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Right for the wrong reasons: Diagnosing syntactic heuristics in natural language inference
Tom McCoy, Ellie Pavlick, and Tal Linzen. 2019 · 2019
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Language models are unsupervised multitask learners
Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019 · 2019
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Tldr: Extreme summarization of scientific documents
Isabel Cachola, Kyle Lo, Arman Cohan, and Daniel S Weld. 2020 · 2020
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Evaluating factuality in generation with dependency-level entailment
Tanya Goyal and Greg Durrett. 2020 · 2020
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What have we achieved on text summarization?
Dandan Huang, Leyang Cui, Sen Yang, Guangsheng Bao, Kun Wang, Jun Xie, and Yue Zhang. 2020 · 2020
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Evaluating the factual consistency of abstractive text summarization
Wojciech Kryściński, Bryan McCann, Caiming Xiong, and Richard Socher. 2020 · 2020
Cited alongside, same era.
Asking and answering questions to evaluate the factual consistency of summaries
Alex Wang, Kyunghyun Cho, and Mike Lewis. 2020 · 2020
Cited alongside, same era.
Cliff: Contrastive learning for improving faithfulness and factuality in abstractive summarization
Shuyang Cao and Lu Wang. 2021 · 2021
Cited alongside, same era.
Summeval: Re-evaluating summarization evaluation
Alexander R Fabbri, Wojciech Kryściński, Bryan McCann, Caiming Xiong, Richard Socher, and Dragomir Radev. 2021 · 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 · 2021
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Multi-lexsum: Real-world summaries of civil rights lawsuits at multiple granularities
Zejiang Shen, Kyle Lo, Lauren Yu, Nathan Dahlberg, Margo Schlanger, and Doug Downey. 2022 · 2022
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Understanding factual errors in summarization: Errors, summarizers, datasets, error detectors
Liyan Tang, Tanya Goyal, Alexander R Fabbri, Philippe Laban, Jiacheng Xu, Semih Yahvuz, Wojciech Kryściński, Justin F Rousseau, and Greg Durrett. 2022 · 2022
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Lamda: Language models for dialog applications
Romal Thoppilan, Daniel De Freitas, Jamie Hall, Noam Shazeer, Apoorv Kulshreshtha, Heng-Tze Cheng, Alicia Jin, Taylor Bos, Leslie Baker, Yu Du, et al. 2022 · 2022
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Chain of thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Ed Chi, Quoc Le, and Denny Zhou. 2022 · 2022
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Thomas Scialom, Paul-Alexis Dray, Sylvain Lamprier, Benjamin Piwowarski, Jacopo Staiano, Alex Wang, and Patrick Gallinari. 2021 · 2021
Cited alongside, same era.
Qmsum: A new benchmark for query-based multi-domain meeting summarization
Ming Zhong, Da Yin, Tao Yu, Ahmad Zaidi, Mutethia Mutuma, Rahul Jha, Ahmed Hassan, Asli Celikyilmaz, Yang Liu, Xipeng Qiu, et al. 2021 · 2021
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Preme: Preference-based meeting exploration through an interactive questionnaire
Negar Arabzadeh, Ali Ahmadvand, Julia Kiseleva, Yang Liu, Ahmed Hassan Awadallah, Ming Zhong, and Milad Shokouhi. 2022 · 2022
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Constitutional ai: Harmlessness from ai feedback
Yuntao Bai, Saurav Kadavath, Sandipan Kundu, Amanda Askell, Jackson Kernion, Andy Jones, Anna Chen, Anna Goldie, Azalia Mirhoseini, Cameron McKinnon, et al. 2022 · 2022
Cited alongside, same era.
Qafacteval: Improved qa-based factual consistency evaluation for summarization
Alexander Richard Fabbri, Chien-Sheng Wu, Wenhao Liu, and Caiming Xiong. 2022 · 2022
Cited alongside, same era.
Dialsummeval: Revisiting summarization evaluation for dialogues
Mingqi Gao and Xiaojun Wan. 2022 · 2022
Cited alongside, same era.
News summarization and evaluation in the era of gpt-3
Tanya Goyal, Junyi Jessy Li, and Greg Durrett. 2022 · 2022
Cited alongside, same era.
The unreliability of explanations in few-shot in-context learning
Xi Ye and Greg Durrett. 2022 · 2022
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Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality
Wei-Lin Chiang, Zhuohan Li, Zi Lin, Ying Sheng, Zhanghao Wu, Hao Zhang, Lianmin Zheng, Siyuan Zhuang, Yonghao Zhuang, Joseph E Gonzalez, et al. 2023 · 2023
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Gptscore: Evaluate as you desire
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Chatgpt as a factual inconsistency evaluator for text summarization
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Alpaca: A strong, replicable instruction-following model
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Introducing mpt-7b: A new standard for open-source, ly usable llms
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Llama: Open and efficient foundation language models
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A prompt pattern catalog to enhance prompt engineering with chatgpt
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