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Recognizing if LLM output can be grounded in evidence is central to many tasks in NLP: retrieval-augmented generation, summarization, document-grounded dialogue, and more.
RoBERTa: A robustly optimized bert pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019 · 1907
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On faithfulness and factuality in abstractive summarization
Joshua Maynez, Shashi Narayan, Bernd Bohnet, and Ryan McDonald. 2020 · 1919
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A large annotated corpus for learning natural language inference
Samuel R. Bowman, Gabor Angeli, Christopher Potts, and Christopher D. Manning. 2015 · 2015
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Abstractive text summarization using sequence-to-sequence rnns and beyond
Ramesh Nallapati, Bowen Zhou, Cicero dos Santos, Caglar Gulcehre, and Bing Xiang. 2016 · 2016
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Annotation artifacts in natural language inference data
Suchin Gururangan, Swabha Swayamdipta, Omer Levy, Roy Schwartz, Samuel Bowman, and Noah A. Smith. 2018 · 2018
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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 · 2018
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A broad-coverage challenge corpus for sentence understanding through inference
Adina Williams, Nikita Nangia, and Samuel Bowman. 2018 · 2018
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Ranking generated summaries by correctness: An interesting but challenging application for natural language inference
Tobias Falke, Leonardo F. R. Ribeiro, Prasetya Ajie Utama, Ido Dagan, and Iryna Gurevych. 2019 · 2019
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ELI5: Long form question answering
Angela Fan, Yacine Jernite, Ethan Perez, David Grangier, Jason Weston, and Michael Auli. 2019 · 2019
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Inherent disagreements in human textual inferences
Ellie Pavlick and Tom Kwiatkowski. 2019 · 2019
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Uncertain natural language inference
Tongfei Chen, Zhengping Jiang, Adam Poliak, Keisuke Sakaguchi, and Benjamin Van Durme. 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 Kryscinski, Bryan McCann, Caiming Xiong, and Richard Socher. 2020 · 2020
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Linguistically-informed transformations (LIT): A method for automatically generating contrast sets
Chuanrong Li, Lin Shengshuo, Zeyu Liu, Xinyi Wu, Xuhui Zhou, and Shane Steinert-Threlkeld. 2020 · 2020
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What can we learn from collective human opinions on natural language inference data?
Yixin Nie, Xiang Zhou, and Mohit Bansal. 2020b · 2020
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Asking and answering questions to evaluate the factual consistency of summaries
Alex Wang, Kyunghyun Cho, and Mike Lewis. 2020 · 2020
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CLIFF: Contrastive learning for improving faithfulness and factuality in abstractive summarization
Shuyang Cao and Lu Wang. 2021 · 2021
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Decontextualization: Making sentences stand-alone
Eunsol Choi, Jennimaria Palomaki, Matthew Lamm, Tom Kwiatkowski, Dipanjan Das, and Michael Collins. 2021 · 2021
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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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Annotating and modeling fine-grained factuality in summarization
Tanya Goyal and Greg Durrett. 2021 · 2021
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Pengcheng He, Jianfeng Gao, and Weizhu Chen. 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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Exploring decomposition for table-based fact verification
Xiaoyu Yang and Xiaodan Zhu. 2021 · 2021
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DocNLI: A large-scale dataset for document-level natural language inference
Wenpeng Yin, Dragomir Radev, and Caiming Xiong. 2021 · 2021
Cited alongside, same era.
MediaSum: A large-scale media interview dataset for dialogue summarization
Chenguang Zhu, Yang Liu, Jie Mei, and Michael Zeng. 2021 · 2021
Cited alongside, same era.
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, Carol Chen, Catherine Olsson, Christopher Olah, Danny Hernandez, Dawn Drain, Deep Ganguli, Dustin Li, Eli Tran-Johnson, Ethan Perez, Jamie Kerr, Jared Mueller, Jeffrey Ladish, Joshua Landau, Kamal Ndousse, Kamile Lukosuite, Liane Lovitt, Michael Sellitto, Nelson Elhage, Nicholas Schiefer, Noemi Mercado, Nova DasSarma, Robert Lasenby, Robin Larson, Sam Ringer, Scott Johnston, Shauna Kravec, Sheer El Showk, Stanislav Fort, Tamera Lanham, Timothy Telleen-Lawton, Tom Conerly, Tom Henighan, Tristan Hume, Samuel R. Bowman, Zac Hatfield-Dodds, Ben Mann, Dario Amodei, Nicholas Joseph, Sam McCandlish, Tom Brown, and Jared Kaplan. 2022 · 2022
Cited alongside, same era.
Hallucinated but factual! inspecting the factuality of hallucinations in abstractive summarization
Meng Cao, Yue Dong, and Jackie Cheung. 2022 · 2022
Cited alongside, same era.
ChatGPT as a Factual Inconsistency Evaluator for Text Summarization
Zheheng Luo, Qianqian Xie, and Sophia Ananiadou. 2023 · 2023
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When not to trust language models: Investigating effectiveness of parametric and non-parametric memories
Alex Mallen, Akari Asai, Victor Zhong, Rajarshi Das, Daniel Khashabi, and Hannaneh Hajishirzi. 2023 · 2023
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Sources of hallucination by large language models on inference tasks
Nick McKenna, Tianyi Li, Liang Cheng, Mohammad Hosseini, Mark Johnson, and Mark Steedman. 2023 · 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 Koh, Mohit Iyyer, Luke Zettlemoyer, and Hannaneh Hajishirzi. 2023 · 2023
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OpenAI. 2023 · 2023
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Scaling instruction-finetuned language models
Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Yunxuan Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, et al. 2022 · 2022
Cited alongside, same era.
QAFactEval: Improved QA-based factual consistency evaluation for summarization
Alexander Fabbri, Chien-Sheng Wu, Wenhao Liu, and Caiming Xiong. 2022 · 2022
Cited alongside, same era.
TRUE: Re-evaluating factual consistency evaluation
Or Honovich, Roee Aharoni, Jonathan Herzig, Hagai Taitelbaum, Doron Kukliansy, Vered Cohen, Thomas Scialom, Idan Szpektor, Avinatan Hassidim, and Yossi Matias. 2022 · 2022
Cited alongside, same era.
SummaC: Re-visiting NLI-based models for inconsistency detection in summarization
Philippe Laban, Tobias Schnabel, Paul N. Bennett, and Marti A. Hearst. 2022 · 2022
Cited alongside, same era.
BRIO: Bringing order to abstractive summarization
Yixin Liu, Pengfei Liu, Dragomir Radev, and Graham Neubig. 2022 · 2022
Cited alongside, same era.
LaMDA: Language Models for Dialog Applications
Romal Thoppilan, Daniel De Freitas, Jamie Hall, Noam M. Shazeer, Apoorv Kulshreshtha, Heng-Tze Cheng, Alicia Jin, Taylor Bos, Leslie Baker, Yu Du, Yaguang Li, Hongrae Lee, Huaixiu Steven Zheng, Amin Ghafouri, Marcelo Menegali, Yanping Huang, Maxim Krikun, Dmitry Lepikhin, James Qin, Dehao Chen, Yuanzhong Xu, Zhifeng Chen, Adam Roberts, Maarten Bosma, Yanqi Zhou, Chung-Ching Chang, I. A. Krivokon, Willard James Rusch, Marc Pickett, Kathleen S. Meier-Hellstern, Meredith Ringel Morris, Tulsee Doshi, Renelito Delos Santos, Toju Duke, Johnny Hartz Søraker, Ben Zevenbergen, Vinodkumar Prabhakaran, Mark Díaz, Ben Hutchinson, Kristen Olson, Alejandra Molina, Erin Hoffman-John, Josh Lee, Lora Aroyo, Ravi Rajakumar, Alena Butryna, Matthew Lamm, V. O. Kuzmina, Joseph Fenton, Aaron Cohen, Rachel Bernstein, Ray Kurzweil, Blaise Aguera-Arcas, Claire Cui, Marian Rogers Croak, Ed Huai hsin Chi, and Quoc Le. 2022 · 2022
Cited alongside, same era.
Evaluating correctness and faithfulness of instruction-following models for question answering
Vaibhav Adlakha, Parishad BehnamGhader, Xing Han Lu, Nicholas Meade, and Siva Reddy. 2023 · 2023
Cited alongside, same era.
Understanding retrieval augmentation for long-form question answering
Hung-Ting Chen, Fangyuan Xu, Shane A Arora, and Eunsol Choi. 2023 · 2023
Cited alongside, same era.
Later among the works it cites.
Improving Wikipedia verifiability with AI
Fabio Petroni, Samuel Broscheit, Aleksandra Piktus, Patrick Lewis, Gautier Izacard, Lucas Hosseini, Jane Dwivedi-Yu, Maria Lomeli, Timo Schick, Michele Bevilacqua, Pierre-Emmanuel Mazaré, Armand Joulin, Edouard Grave, and Sebastian Riedel. 2023 · 2023
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A survey of hallucination in large foundation models
Vipula Rawte, Amit Sheth, and Amitava Das. 2023 · 2023
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Summarizing, simplifying, and synthesizing medical evidence using GPT-3 (with varying success)
Chantal Shaib, Millicent Li, Sebastian Joseph, Iain Marshall, Junyi Jessy Li, and Byron Wallace. 2023 · 2023
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Less likely brainstorming: Using language models to generate alternative hypotheses
Liyan Tang, Yifan Peng, Yanshan Wang, Ying Ding, Greg Durrett, and Justin Rousseau. 2023b · 2023
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Stanford Alpaca: An Instruction-following LLaMA model
Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li, Carlos Guestrin, Percy Liang, and Tatsunori B. Hashimoto. 2023 · 2023
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Gemini: A family of highly capable multimodal models
Gemini Team, Rohan Anil, Sebastian Borgeaud, Yonghui Wu, Jean-Baptiste Alayrac, Jiahui Yu, Radu Soricut, Johan Schalkwyk, Andrew M. Dai, Anja Hauth, and et al. 2023 · 2023
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Factcheck-GPT: End-to-End Fine-Grained Document-Level Fact-Checking and Correction of LLM Output
Yuxia Wang, Revanth Gangi Reddy, Zain Muhammad Mujahid, Arnav Arora, Aleksandr Rubashevskii, Jiahui Geng, Osama Mohammed Afzal, Liangming Pan, Nadav Borenstein, Aditya Pillai, Isabelle Augenstein, Iryna Gurevych, and Preslav Nakov. 2023 · 2023
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Large language models are better reasoners with self-verification
Yixuan Weng, Minjun Zhu, Fei Xia, Bin Li, Shizhu He, Shengping Liu, Bin Sun, Kang Liu, and Jun Zhao. 2023 · 2023
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AlignScore: Evaluating factual consistency with a unified alignment function
Yuheng Zha, Yichi Yang, Ruichen Li, and Zhiting Hu. 2023 · 2023
Later among the works it cites.
Context-faithful prompting for large language models
Wenxuan Zhou, Sheng Zhang, Hoifung Poon, and Muhao Chen. 2023 · 2023
Later among the works it cites.
Benchmarking large language models in retrieval-augmented generation
Jiawei Chen, Hongyu Lin, Xianpei Han, and Le Sun. 2024 · 2024
Closest in time.
AmbiFC: Fact-Checking Ambiguous Claims with Evidence
Max Glockner, Ieva Staliūnaitė, James Thorne, Gisela Vallejo, Andreas Vlachos, and Iryna Gurevych. 2024 · 2024
Closest in time.
A chain-of-thought is as strong as its weakest link: A benchmark for verifiers of reasoning chains
Alon Jacovi, Yonatan Bitton, Bernd Bohnet, Jonathan Herzig, Or Honovich, Michael Tseng, Michael Collins, Roee Aharoni, and Mor Geva. 2024 · 2024
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Albert Q. Jiang, Alexandre Sablayrolles, Antoine Roux, Arthur Mensch, Blanche Savary, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Emma Bou Hanna, Florian Bressand, Gianna Lengyel, Guillaume Bour, Guillaume Lample, Lélio Renard Lavaud, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Sandeep Subramanian, Sophia Yang, Szymon Antoniak, Teven Le Scao, Théophile Gervet, Thibaut Lavril, Thomas Wang, Timothée Lacroix, and William El Sayed. 2024 · 2024
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ExpertQA: Expert-curated questions and attributed answers
Chaitanya Malaviya, Subin Lee, Sihao Chen, Elizabeth Sieber, Mark Yatskar, and Dan Roth. 2024 · 2024
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Minds versus machines: Rethinking entailment verification with language models
Soumya Sanyal, Tianyi Xiao, Jiacheng Liu, Wenya Wang, and Xiang Ren. 2024 · 2024
Closest in time.
TofuEval: Evaluating Hallucinations of LLMs on Topic-Focused Dialogue Summarization
Liyan Tang, Igor Shalyminov, Amy Wing mei Wong, Jon Burnsky, Jake W. Vincent, Yu’an Yang, Siffi Singh, Song Feng, Hwanjun Song, Hang Su, Lijia Sun, Yi Zhang, Saab Mansour, and Kathleen McKeown. 2024 · 2024
Closest in time.
Benchmarking large language models for news summarization
Tianyi Zhang, Faisal Ladhak, Esin Durmus, Percy Liang, Kathleen McKeown, and Tatsunori B. Hashimoto. 2024 · 2024
Closest in time.