🔗 Who cites «Reducing Quantity Hallucinations in Abstractive Summarization»

59 papers of our library cite it (of the 141,840 whose bibliographies we read) · 13 with a page here, the most cited first · ← back to «Reducing Quantity Hallucinations in Abstractive Summarization»

1. Survey of Hallucination in Natural Language GenerationZiwei Ji, Nayeon Lee, Rita Frieske, et al.2022 · 5,957 citations
2. Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language ModelsYue Zhang, Yafu Li, Leyang Cui, et al.2023 · 966 citations
3. HaluEval: A Large-Scale Hallucination Evaluation Benchmark for Large Language ModelsJunyi Li, Xiaoxue Cheng, Wayne Xin Zhao, et al.2023 · 647 citations
4. QuestEval: Summarization Asks for Fact-based EvaluationThomas Scialom, Paul-Alexis Dray, Sylvain Lamprier, et al.2021 · 354 citations
5. TRUE: Re-evaluating Factual Consistency EvaluationOr Honovich, Roee Aharoni, Jonathan Herzig, et al.2022 · 276 citations
6. Annotating and Modeling Fine-grained Factuality in SummarizationTanya Goyal, Greg Durrett2021 · 215 citations
7. Hallucinated but Factual! Inspecting the Factuality of Hallucinations in Abstractive SummarizationMeng Cao, Yue Dong, Jackie Cheung2022 · 167 citations
8. LLMLingua-2: Data Distillation for Efficient and Faithful Task-Agnostic Prompt CompressionZhuoshi Pan, Qianhui Wu, Huiqiang Jiang, et al.2024 · 159 citations
9. Increasing Faithfulness in Knowledge-Grounded Dialogue with Controllable FeaturesHannah Rashkin, David Reitter, Gaurav Singh Tomar, et al.2021 · 140 citations
10. A Stitch in Time Saves Nine: Detecting and Mitigating Hallucinations of LLMs by Validating Low-Confidence GenerationNeeraj Varshney; Wenlin Yao; Hongming Zhang; Jianshu Chen;, Dong Yu2023 · 139 citations
11. GO FIGURE: A Meta Evaluation of Factuality in SummarizationSaadia Gabriel, Asli Celikyilmaz, Rahul Jha, et al.2021 · 129 citations
12. Understanding Factual Errors in Summarization: Errors, Summarizers, Datasets, Error DetectorsLiyan Tang, Tanya Goyal, Alex Fabbri, et al.2023 · 126 citations
13. Language Generation Models Can Cause Harm: So What Can We Do About It? An Actionable SurveySachin Kumar, Vidhisha Balachandran, Lucille Njoo, et al.2023 · 87 citations

46 more papers of our library cite it; they have no page here yet.

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