🔗 Who cites «Multi-Fact Correction in Abstractive Text Summarization»

65 papers of our library cite it (of the 141,840 whose bibliographies we read) · 11 with a page here, the most cited first · ← back to «Multi-Fact Correction in Abstractive Text Summarization»

1. Survey of Hallucination in Natural Language GenerationZiwei Ji, Nayeon Lee, Rita Frieske, et al.2022 · 5,957 citations
2. CLIFF: Contrastive Learning for Improving Faithfulness and Factuality in Abstractive SummarizationShuyang Cao, Lu Wang2021 · 258 citations
3. Trusting Your Evidence: Hallucinate Less with Context-aware DecodingWeijia Shi, Xiaochuang Han, Mike Lewis, et al.2024 · 240 citations
4. Factual Error Correction for Abstractive Summarization ModelsMeng Cao, Yue Dong, Jiapeng Wu, et al.2020 · 219 citations
5. Annotating and Modeling Fine-grained Factuality in SummarizationTanya Goyal, Greg Durrett2021 · 215 citations
6. Enhancing Factual Consistency of Abstractive SummarizationChenguang Zhu, William Hinthorn, Ruochen Xu, et al.2021 · 188 citations
7. Hallucinated but Factual! Inspecting the Factuality of Hallucinations in Abstractive SummarizationMeng Cao, Yue Dong, Jackie Cheung2022 · 167 citations
8. A Token-level Reference-free Hallucination Detection Benchmark for Free-form Text GenerationTianyu Liu, Yizhe Zhang, Chris Brockett, et al.2022 · 140 citations
9. Structure-Aware Abstractive Conversation Summarization via Discourse and Action GraphsJiaao Chen, Diyi Yang2021 · 135 citations
10. FactPEGASUS: Factuality-Aware Pre-training and Fine-tuning for Abstractive SummarizationDavid Wan, Mohit Bansal2022 · 101 citations
11. Survey on Hallucinations in Large Language Models (大语言模型的幻觉问题研究综述)Zeyuan Liu (刘泽垣), Pengjiang Wang (王鹏江), Xiaobin Song (宋晓斌), et al.2024

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

This page in Markdown → https://md.research.elamur.ai/free/en/papers/dong-2020-multi-fact-correction-abstractive-text/cited-by/