🧑‍🔬 Who cites Feng Nan

248 citations of their works from 230 papers of our library (of the 141,840 whose bibliographies we read) · 18 with a page here, the most cited first · ← Feng Nan

Survey of Hallucination in Natural Language GenerationACM Computing SurveysZiwei Ji, Nayeon Lee, Rita Frieske, et al.2022 · 5,957 citations
A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open QuestionsACM Transactions on Information SystemsLei Huang, Weijiang Yu, Weitao Ma, et al.2023 · 3,222 citations
CLIFF: Contrastive Learning for Improving Faithfulness and Factuality in Abstractive SummarizationConference on Empirical Methods in Natural Language Processing (EMNLP)Shuyang Cao, Lu Wang2021 · 258 citations
Trusting Your Evidence: Hallucinate Less with Context-aware DecodingConference of the North American Chapter of the Association for Computational Linguistics (NAACL)Weijia Shi, Xiaochuang Han, Mike Lewis, et al.2024 · 240 citations
Q²: Evaluating Factual Consistency in Knowledge-Grounded Dialogues via Question Generation and Question AnsweringConference on Empirical Methods in Natural Language Processing (EMNLP)Or Honovich, Leshem Choshen, Roee Aharoni, et al.2021 · 195 citations
Hallucinated but Factual! Inspecting the Factuality of Hallucinations in Abstractive SummarizationAnnual Meeting of the Association for Computational Linguistics (ACL)Meng Cao, Yue Dong, Jackie Cheung2022 · 167 citations
1. ChatGPT as a Factual Inconsistency Evaluator for Text SummarizationarXiv:2303.15621Zheheng Luo, Qianqian Xie, Sophia Ananiadou2023 · 160 citations
   Improving Factual Consistency of Abstractive Summarization via Question AnsweringAnnual Meeting of the Association for Computational Linguistics (ACL)Feng Nan, Cicero Nogueira dos Santos, Henghui Zhu, et al.2021 · 128 citations
   Understanding Factual Errors in Summarization: Errors, Summarizers, Datasets, Error DetectorsAnnual Meeting of the Association for Computational Linguistics (ACL)Liyan Tang, Tanya Goyal, Alex Fabbri, et al.2023 · 126 citations
   JointCL: A Joint Contrastive Learning Framework for Zero-Shot Stance DetectionAnnual Meeting of the Association for Computational Linguistics (ACL)Bin Liang, Qinglin Zhu, Xiang Li, et al.2022 · 119 citations
   Large Language Models Enable Few-Shot ClusteringTransactions of the Association for Computational Linguistics (TACL)Vijay Viswanathan, Kiril Gashteovski, Carolin Lawrence, et al.2024 · 109 citations
   FactPEGASUS: Factuality-Aware Pre-training and Fine-tuning for Abstractive SummarizationConference of the North American Chapter of the Association for Computational Linguistics (NAACL)David Wan, Mohit Bansal2022 · 101 citations
   Evaluating Attribution in Dialogue Systems: The BEGIN BenchmarkTransactions of the Association for Computational Linguistics (TACL)Nouha Dziri, Hannah Rashkin, Tal Linzen, et al.2022 · 99 citations
   Language Generation Models Can Cause Harm: So What Can We Do About It? An Actionable SurveyConference of the European Chapter of the Association for Computational Linguistics (EACL)Sachin Kumar, Vidhisha Balachandran, Lucille Njoo, et al.2023 · 87 citations
   Evaluating Large Language Models for Health-related Queries with PresuppositionsFindings of the Association for Computational LinguisticsNavreet Kaur, Monojit Choudhury, Danish Pruthi2024 · 15 citations
   GUMsley: Evaluating Entity Salience in Summarization for 12 English GenresConference of the European Chapter of the Association for Computational Linguistics (EACL)Jessica Lin, Amir Zeldes2024 · 8 citations
2. Survey of Tool Use in Large Language Models (大语言模型的工具使用综述)Acta Automatica Sinica (自动化学报)Yining Zheng (郑逸宁), Zhen Yu (余镇), Bufan Li (李不凡), et al.2025
   Survey on Hallucinations in Large Language Models (大语言模型的幻觉问题研究综述)Journal of SoftwareZeyuan Liu (刘泽垣), Pengjiang Wang (王鹏江), Xiaobin Song (宋晓斌), et al.2024

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