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Large language models (LLMs) often produce unsupported or unverifiable content, known as "hallucinations." To mitigate this, retrieval-augmented LLMs incorporate citations, grounding the content in verifiable sources.
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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Teaching machines to read and comprehend
Karl Moritz Hermann, Tomás Kociský, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, and Phil Blunsom. 2015 · 2015
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BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
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ELI5: Long Form Question Answering
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FEQA: A question answering evaluation framework for faithfulness assessment in abstractive summarization
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Query-controllable video summarization
Jia-Hong Huang and Marcel Worring. 2020 · 2020
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Dense passage retrieval for open-domain question answering
Vladimir Karpukhin, Barlas Oguz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih. 2020 · 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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BERTScore: Evaluating Text Generation with BERT
Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q Weinberger, and Yoav Artzi. 2020 · 2020
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Deberta: Decoding-Enhanced Bert with Disentangled Attention
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QuestEval: Summarization asks for fact-based evaluation
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BARTScore: Evaluating Generated Text as Text Generation
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Scaling up query-focused summarization to meet open-domain question answering
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Attributed question answering: Evaluation and modeling for attributed large language models
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QAFactEval: Improved QA-Based factual consistency evaluation for summarization
Alexander Fabbri, Chien-Sheng Wu, Wenhao Liu, and Caiming Xiong. 2022 · 2022
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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
Measuring attribution in natural language generation models
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INSTRUCTSCORE: Towards explainable text generation evaluation with automatic feedback
Wenda Xu, Danqing Wang, Liangming Pan, Zhenqiao Song, Markus Freitag, William Wang, and Lei Li. 2023 · 2023
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Automatic evaluation of attribution by large language models
Xiang Yue, Boshi Wang, Ziru Chen, Kai Zhang, Yu Su, and Huan Sun. 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
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Unsupervised dense information retrieval with contrastive learning
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Large language models are zero-shot reasoners
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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.
Wei Li, Wenhao Wu, Moye Chen, Jiachen Liu, Xinyan Xiao, and Hua Wu. 2022 · 2022
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Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed H. Chi, Quoc V. Le, and Denny Zhou. 2022 · 2022
Cited alongside, same era.
Self-rag: Learning to retrieve, generate, and critique through self-reflection
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Towards verifiable generation: A benchmark for knowledge-aware language model attribution
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AttributionBench: How hard is automatic attribution evaluation?
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Citekit: A modular toolkit for large language model citation generation
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Efficient citer: Tuning large language models for enhanced answer quality and verification
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Effective large language model adaptation for improved grounding and citation generation
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Enhancing interactive image retrieval with query rewriting using large language models and vision language models
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