🔗 Who cites «Improving Faithfulness in Abstractive Summarization with Contrast Candidate Generation and Selection»

66 papers of our library cite it (of the 141,840 whose bibliographies we read) · 9 with a page here, the most cited first · ← back to «Improving Faithfulness in Abstractive Summarization with Contrast Candidate Generation and Selection»

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. Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models' AlignmentYang Liu; Yuanshun Yao; Jean-Francois Ton; Xiaoying Zhang; Ruocheng Guo; Hao Cheng; Yegor Klochkov; Muhammad Faaiz Taufiq;, Hang Li2023 · 220 citations
4. Measure and Improve Robustness in NLP Models: A SurveyXuezhi Wang, Haohan Wang, Diyi Yang2022 · 148 citations
5. 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
6. Improving Factual Consistency of Abstractive Summarization via Question AnsweringFeng Nan, Cicero Nogueira dos Santos, Henghui Zhu, et al.2021 · 128 citations
7. Understanding Factual Errors in Summarization: Errors, Summarizers, Datasets, Error DetectorsLiyan Tang, Tanya Goyal, Alex Fabbri, et al.2023 · 126 citations
8. FactPEGASUS: Factuality-Aware Pre-training and Fine-tuning for Abstractive SummarizationDavid Wan, Mohit Bansal2022 · 101 citations
9. 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

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

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