---
title: "Who cites Jackie Chi Kit Cheung"
description: "471 papers of the Elamur Research library cite the works of Jackie Chi Kit Cheung, found in their bibliographies; the most cited first."
canonical: "https://research.elamur.ai/free/en/authors/jackie-chi-kit-cheung/cited-by/"
---
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# Who cites Jackie Chi Kit Cheung

533 citations of their works from 471 papers of our library (of the 140,884 whose bibliographies we read) · 79 with a page here, the most cited first · [← Jackie Chi Kit Cheung](https://md.research.elamur.ai/free/en/authors/jackie-chi-kit-cheung/)

1. [**Survey of Hallucination in Natural Language Generation**Ziwei Ji, Nayeon Lee, Rita Frieske, et al.*2022 · 5,947 citations*](https://md.research.elamur.ai/free/en/papers/ji-2022-survey-hallucination-natural-language-generation/)
2. [**Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models**Yue Zhang, Yafu Li, Leyang Cui, et al.*2023 · 962 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2023-siren-s-song-ai-ocean/)
3. [**HaluEval: A Large-Scale Hallucination Evaluation Benchmark for Large Language Models**Junyi Li, Xiaoxue Cheng, Wayne Xin Zhao, et al.*2023 · 646 citations*](https://md.research.elamur.ai/free/en/papers/li-2023-halueval-large-scale-hallucination-evaluation/)
4. [**Benchmarking Large Language Models in Retrieval-Augmented Generation**Jiawei Chen, Hongyu Lin, Xianpei Han, et al.*2023 · 624 citations*](https://md.research.elamur.ai/free/en/papers/chen-2023-benchmarking-large-language-models-retrieval/)
5. [**Large Legal Fictions: Profiling Legal Hallucinations in Large Language Models**Matthew Dahl, Varun Magesh, Mirac Suzgun, et al.*2024 · 431 citations*](https://md.research.elamur.ai/free/en/papers/dahl-2024-large-legal-fictions-profiling-legal/)
6. [**Improving the Domain Adaptation of Retrieval Augmented Generation (RAG) Models for Open Domain Question Answering**Shamane Siriwardhana, Rivindu Weerasekera, Elliott Wen, et al.*2023 · 403 citations*](https://md.research.elamur.ai/free/en/papers/siriwardhana-2023-improving-domain-adaptation-retrieval-augmented/)
7. [**Discourse-Aware Neural Extractive Text Summarization**Jiacheng Xu, Zhe Gan, Yu Cheng, et al.*2019 · 382 citations*](https://md.research.elamur.ai/free/en/papers/xu-2019-discourse-aware-neural-extractive-text/)
8. [**RARR: Researching and Revising What Language Models Say, Using Language Models**Luyu Gao, Zhuyun Dai, Panupong Pasupat, et al.*2023 · 351 citations*](https://md.research.elamur.ai/free/en/papers/gao-2023-rarr-researching-revising-what-language/)
9. [**Reformulating Unsupervised Style Transfer as Paraphrase Generation**Kalpesh Krishna, John Wieting, Mohit Iyyer*2020 · 348 citations*](https://md.research.elamur.ai/free/en/papers/krishna-2020-reformulating-unsupervised-style-transfer-as/)
10. [**CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing**Zhibin Gou, Zhihong Shao, Yeyun Gong, et al.*2023 · 315 citations*](https://md.research.elamur.ai/free/en/papers/gou-2023-critic-large-language-models-self/)
11. [**A Survey of the State of Explainable AI for Natural Language Processing**Marina Danilevsky, Kun Qian, Ranit Aharonov, et al.*2020 · 290 citations*](https://md.research.elamur.ai/free/en/papers/danilevsky-2020-survey-state-explainable-ai-natural/)
12. [**On-Policy Distillation of Language Models: Learning from Self-Generated Mistakes**Rishabh Agarwal, Nino Vieillard, Yongchao Zhou, et al.*2023 · 288 citations*](https://md.research.elamur.ai/free/en/papers/agarwal-2023-policy-distillation-language-models-learning/)
13. [**Queens are Powerful too: Mitigating Gender Bias in Dialogue Generation**Emily Dinan, Angela Fan, Adina Williams, et al.*2020 · 265 citations*](https://md.research.elamur.ai/free/en/papers/dinan-2020-queens-are-powerful-too-mitigating/)
14. [**CLIFF: Contrastive Learning for Improving Faithfulness and Factuality in Abstractive Summarization**Shuyang Cao, Lu Wang*2021 · 257 citations*](https://md.research.elamur.ai/free/en/papers/cao-2021-cliff-contrastive-learning-improving-faithfulness/)
15. [**CorefQA: Coreference Resolution as Query-based Span Prediction**Wei Wu, Fei Wang, Arianna Yuan, et al.*2020 · 253 citations*](https://md.research.elamur.ai/free/en/papers/wu-2020-corefqa-coreference-resolution-as-query/)
16. [**Hallucinations in Large Multilingual Translation Models**Nuno M. Guerreiro, Duarte M. Alves, Jonas Waldendorf, et al.*2023 · 244 citations*](https://md.research.elamur.ai/free/en/papers/guerreiro-2023-hallucinations-large-multilingual-translation-models/)
17. [**Trusting Your Evidence: Hallucinate Less with Context-aware Decoding**Weijia Shi, Xiaochuang Han, Mike Lewis, et al.*2024 · 240 citations*](https://md.research.elamur.ai/free/en/papers/shi-2024-trusting-your-evidence-hallucinate-less/)
18. [**Neural CRF Model for Sentence Alignment in Text Simplification**Chao Jiang, Mounica Maddela, Wuwei Lan, et al.*2020 · 227 citations*](https://md.research.elamur.ai/free/en/papers/jiang-2020-neural-crf-model-sentence-alignment/)
19. [**How Language Model Hallucinations Can Snowball**Muru Zhang, Ofir Press, William Merrill, et al.*2023 · 221 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2023-language-model-hallucinations-snowball/)
20. [**Factual Error Correction for Abstractive Summarization Models**Meng Cao, Yue Dong, Jiapeng Wu, et al.*2020 · 217 citations*](https://md.research.elamur.ai/free/en/papers/cao-2020-factual-error-correction-abstractive-summarization/)
21. [**Annotating and Modeling Fine-grained Factuality in Summarization**Tanya Goyal, Greg Durrett*2021 · 215 citations*](https://md.research.elamur.ai/free/en/papers/goyal-2021-annotating-modeling-fine-grained-factuality/)
22. [**Towards a Unified Multi-Dimensional Evaluator for Text Generation**Ming Zhong, Yang Liu, Da Yin, et al.*2022 · 204 citations*](https://md.research.elamur.ai/free/en/papers/zhong-2022-unified-multi-dimensional-evaluator-text/)
23. [**Evaluating Large Language Models: A Comprehensive Survey**Zishan Guo, Renren Jin, Chuang Liu, et al.*2023 · 177 citations*](https://md.research.elamur.ai/free/en/papers/guo-2023-evaluating-large-language-models-comprehensive/)
24. [**Survey on Factuality in Large Language Models: Knowledge, Retrieval and Domain-Specificity**Cunxiang Wang, Xiaoze Liu, Yuanhao Yue, et al.*2023 · 169 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-survey-factuality-large-language-models/)
25. [**Hallucinated but Factual! Inspecting the Factuality of Hallucinations in Abstractive Summarization**Meng Cao, Yue Dong, Jackie Cheung*2022 · 167 citations*](https://md.research.elamur.ai/free/en/papers/cao-2022-hallucinated-but-factual-inspecting-factuality/)
26. [**Is Reinforcement Learning (Not) for Natural Language Processing: Benchmarks, Baselines, and Building Blocks for Natural Language Policy Optimization**Rajkumar Ramamurthy, Prithviraj Ammanabrolu, Kiante Brantley, et al.*2022 · 166 citations*](https://md.research.elamur.ai/free/en/papers/ramamurthy-2022-reinforcement-learning-natural-language-processing/)
27. [**Controllable Text Simplification with Explicit Paraphrasing**Mounica Maddela, Fernando Alva-Manchego, Wei Xu*2021 · 164 citations*](https://md.research.elamur.ai/free/en/papers/maddela-2021-controllable-text-simplification-explicit-paraphrasing/)
28. [**Revisiting Joint Modeling of Cross-document Entity and Event Coreference Resolution**Shany Barhom, Vered Shwartz, Alon Eirew, et al.*2019 · 164 citations*](https://md.research.elamur.ai/free/en/papers/barhom-2019-revisiting-joint-modeling-cross-document/)
29. [**Are Natural Language Inference Models IMPPRESsive? Learning IMPlicature and PRESupposition**Paloma Jeretic, Alex Warstadt, Suvrat Bhooshan, et al.*2020 · 161 citations*](https://md.research.elamur.ai/free/en/papers/jeretic-2020-are-natural-language-inference-models/)
30. [**MiniCheck: Efficient Fact-Checking of LLMs on Grounding Documents**Liyan Tang, Philippe Laban, Greg Durrett*2024 · 159 citations*](https://md.research.elamur.ai/free/en/papers/tang-2024-minicheck-efficient-fact-checking-llms/)
31. [**When Can LLMs Actually Correct Their Own Mistakes? A Critical Survey of Self-Correction of LLMs**Ryo Kamoi, Yusen Zhang, Nan Zhang, et al.*2024 · 159 citations*](https://md.research.elamur.ai/free/en/papers/kamoi-2024-when-can-llms-actually-correct/)
32. [**The (Un)Suitability of Automatic Evaluation Metrics for Text Simplification**Fernando Alva-Manchego, Carolina Scarton, Lucia Specia*2021 · 156 citations*](https://md.research.elamur.ai/free/en/papers/manchego-2021-un-suitability-automatic-evaluation-metrics/)
33. [**BLEnD: A Benchmark for LLMs on Everyday Knowledge in Diverse Cultures and Languages**Junho Myung, Nayeon Lee, Yi Zhou, et al.*2024 · 147 citations*](https://md.research.elamur.ai/free/en/papers/myung-2024-blend-benchmark-llms-everyday-knowledge/)
34. [**MiniLLM: On-Policy Distillation of Large Language Models**Yuxian Gu, Li Dong, Furu Wei, et al.*2023 · 143 citations*](https://md.research.elamur.ai/free/en/papers/gu-2023-minillm-policy-distillation-large-language/)
35. [**A Token-level Reference-free Hallucination Detection Benchmark for Free-form Text Generation**Tianyu Liu, Yizhe Zhang, Chris Brockett, et al.*2022 · 140 citations*](https://md.research.elamur.ai/free/en/papers/liu-2022-token-level-reference-free-hallucination/)
36. [**A Stitch in Time Saves Nine: Detecting and Mitigating Hallucinations of LLMs by Validating Low-Confidence Generation**Neeraj Varshney; Wenlin Yao; Hongming Zhang; Jianshu Chen;, Dong Yu*2023 · 139 citations*](https://md.research.elamur.ai/free/en/papers/varshney-2023-stitch-time-saves-nine-detecting/)
37. [**Paragraph-level Simplification of Medical Texts**Ashwin Devaraj, Iain Marshall, Byron Wallace, et al.*2021 · 139 citations*](https://md.research.elamur.ai/free/en/papers/devaraj-2021-paragraph-level-simplification-medical-texts/)
38. [**Deductive Verification of Chain-of-Thought Reasoning**Zhan Ling, Yunhao Fang, Xuanlin Li, et al.*2023 · 128 citations*](https://md.research.elamur.ai/free/en/papers/ling-2023-deductive-verification-chain-thought-reasoning/)
39. [**Understanding Factual Errors in Summarization: Errors, Summarizers, Datasets, Error Detectors**Liyan Tang, Tanya Goyal, Alex Fabbri, et al.*2023 · 126 citations*](https://md.research.elamur.ai/free/en/papers/tang-2023-understanding-factual-errors-summarization-errors/)
40. [**Fine-tuning Language Models for Factuality**Katherine Tian, Eric Mitchell, Huaxiu Yao, et al.*2023 · 120 citations*](https://md.research.elamur.ai/free/en/papers/tian-2023-fine-tuning-language-models-factuality/)
41. [**Encode, Tag, Realize: High-Precision Text Editing**Eric Malmi, Sebastian Krause, Sascha Rothe, et al.*2019 · 118 citations*](https://md.research.elamur.ai/free/en/papers/malmi-2019-encode-tag-realize-high-precision/)
42. [**MUSS: Multilingual Unsupervised Sentence Simplification by Mining Paraphrases**Louis Martin, Angela Fan, Éric de la Clergerie, et al.*2022 · 117 citations*](https://md.research.elamur.ai/free/en/papers/martin-2022-muss-multilingual-unsupervised-sentence-simplification/)
43. [**DistiLLM: Towards Streamlined Distillation for Large Language Models**Jongwoo Ko, Sungnyun Kim, Tianyi Chen, et al.*2024 · 111 citations*](https://md.research.elamur.ai/free/en/papers/ko-2024-distillm-towards-streamlined-distillation-large/)
44. [**LENS: A Learnable Evaluation Metric for Text Simplification**Mounica Maddela, Yao Dou, David Heineman, et al.*2023 · 103 citations*](https://md.research.elamur.ai/free/en/papers/maddela-2023-lens-learnable-evaluation-metric-text/)
45. [**The Goldilocks of Pragmatic Understanding: Fine-Tuning Strategy Matters for Implicature Resolution by LLMs**Laura Ruis, Akbir Khan, Stella Biderman, et al.*2022 · 86 citations*](https://md.research.elamur.ai/free/en/papers/ruis-2022-goldilocks-pragmatic-understanding-fine-tuning/)
46. [**Evaluating prose style transfer with the Bible**Keith Carlson, Allen Riddell, Daniel Rockmore*2017 · 77 citations*](https://md.research.elamur.ai/free/en/papers/carlson-2017-evaluating-prose-style-transfer-bible/)
47. [**PUB: A Pragmatics Understanding Benchmark for Assessing LLMs' Pragmatics Capabilities**Settaluri Sravanthi, Meet Doshi, Pavan Tankala, et al.*2024 · 75 citations*](https://md.research.elamur.ai/free/en/papers/sravanthi-2024-pub-pragmatics-understanding-benchmark-assessing/)
48. [**Idiomatic Expression Identification using Semantic Compatibility**Ziheng Zeng, Suma Bhat*2021 · 75 citations*](https://md.research.elamur.ai/free/en/papers/zeng-2021-idiomatic-expression-identification-semantic-compatibility/)
49. [**Which Linguist Invented the Lightbulb? Presupposition Verification for Question-Answering**Najoung Kim, Ellie Pavlick, Burcu Karagol Ayan, et al.*2021 · 63 citations*](https://md.research.elamur.ai/free/en/papers/kim-2021-which-linguist-invented-lightbulb-presupposition/)
50. [**Cross-document Coreference Resolution over Predicted Mentions**Arie Cattan, Alon Eirew, Gabriel Stanovsky, et al.*2021 · 62 citations*](https://md.research.elamur.ai/free/en/papers/cattan-2021-cross-document-coreference-resolution-over/)
51. [**Event Coreference Resolution with their Paraphrases and Argument-aware Embeddings**Yutao Zeng, Xiaolong Jin, Saiping Guan, et al.*2020 · 50 citations*](https://md.research.elamur.ai/free/en/papers/zeng-2020-event-coreference-resolution-their-paraphrases/)
52. [**WEC: Deriving a Large-scale Cross-document Event Coreference dataset from Wikipedia**Alon Eirew, Arie Cattan, Ido Dagan*2021 · 47 citations*](https://md.research.elamur.ai/free/en/papers/eirew-2021-wec-deriving-large-scale-cross/)
53. [**Paraphrasing vs Coreferring: Two Sides of the Same Coin**Yehudit Meged, Avi Caciularu, Vered Shwartz, et al.*2020 · 47 citations*](https://md.research.elamur.ai/free/en/papers/meged-2020-paraphrasing-vs-coreferring-two-sides/)
54. [**ChatGPT vs Human-authored Text: Insights into Controllable Text Summarization and Sentence Style Transfer**Dongqi Liu, Vera Demberg*2023 · 42 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-chatgpt-vs-human-authored-text/)
55. [**Exploiting Document Structures and Cluster Consistencies for Event Coreference Resolution**Hieu Minh Tran, Duy Phung, Thien Huu Nguyen*2021 · 36 citations*](https://md.research.elamur.ai/free/en/papers/tran-2021-exploiting-document-structures-cluster-consistencies/)
56. [**Focus on what matters: Applying Discourse Coherence Theory to Cross Document Coreference**William Held, Dan Iter, Dan Jurafsky*2021 · 34 citations*](https://md.research.elamur.ai/free/en/papers/held-2021-focus-what-matters-applying-discourse/)
57. [**Pairwise Representation Learning for Event Coreference**Xiaodong Yu, Wenpeng Yin, Dan Roth*2022 · 31 citations*](https://md.research.elamur.ai/free/en/papers/yu-2022-pairwise-representation-learning-event-coreference/)
58. [**Realistic Evaluation Principles for Cross-document Coreference Resolution**Arie Cattan, Alon Eirew, Gabriel Stanovsky, et al.*2021 · 31 citations*](https://md.research.elamur.ai/free/en/papers/cattan-2021-realistic-evaluation-principles-cross-document/)
59. [**Elaborative Simplification as Implicit Questions Under Discussion**Yating Wu, William Sheffield, Kyle Mahowald, et al.*2023 · 18 citations*](https://md.research.elamur.ai/free/en/papers/wu-2023-elaborative-simplification-as-implicit-questions/)
60. [**A Rationale-centric Counterfactual Data Augmentation Method for Cross-Document Event Coreference Resolution**Bowen Ding, Qingkai Min, Shengkun Ma, et al.*2024 · 17 citations*](https://md.research.elamur.ai/free/en/papers/ding-2024-rationale-centric-counterfactual-data-augmentation/)
61. [**2*n is better than n²: Decomposing Event Coreference Resolution into Two Tractable Problems**Shafiuddin Rehan Ahmed, Abhijnan Nath, James H. Martin, et al.*2023 · 17 citations*](https://md.research.elamur.ai/free/en/papers/ahmed-2023-2-n-is-better-than/)
62. [**CAW-coref: Conjunction-Aware Word-level Coreference Resolution**Karel D'Oosterlinck, Semere Kiros Bitew, Brandon Papineau, et al.*2023 · 17 citations*](https://md.research.elamur.ai/free/en/papers/oosterlinck-2023-caw-coref-conjunction-aware-word/)
63. [**Okay, Let's Do This! Modeling Event Coreference with Generated Rationales and Knowledge Distillation**Abhijnan Nath, Shadi Manafi Avari, Avyakta Chelle, et al.*2024 · 16 citations*](https://md.research.elamur.ai/free/en/papers/nath-2024-okay-let-s-do-this/)
64. [**Cross-Document Event Coreference Resolution on Discourse Structure**Xinyu Chen, Sheng Xu, Peifeng Li, et al.*2023 · 16 citations*](https://md.research.elamur.ai/free/en/papers/chen-2023-cross-document-event-coreference-resolution/)
65. [**Improving Event Coreference Resolution Using Document-level and Topic-level Information**Sheng Xu, Peifeng Li, Qiaoming Zhu*2022 · 15 citations*](https://md.research.elamur.ai/free/en/papers/xu-2022-improving-event-coreference-resolution-document/)
66. [**Investigating the Performance of Transformer-Based NLI Models on Presuppositional Inferences**Jad Kabbara, Jackie Chi Kit Cheung*2022 · 12 citations*](https://md.research.elamur.ai/free/en/papers/kabbara-2022-investigating-performance-transformer-based-nli/)
67. [**NoEl: An Annotated Corpus for Noun Ellipsis in English**Payal Khullar, Kushal Majmundar, Manish Shrivastava*2020 · 12 citations*](https://md.research.elamur.ai/free/en/papers/khullar-2020-noel-annotated-corpus-noun-ellipsis/)
68. [**Pregnant Questions: The Importance of Pragmatic Awareness in Maternal Health Question Answering**Neha Srikanth, Rupak Sarkar, Heran Mane, et al.*2024 · 11 citations*](https://md.research.elamur.ai/free/en/papers/srikanth-2024-pregnant-questions-importance-pragmatic-awareness/)
69. [**Findings of the Fourth Shared Task on Multilingual Coreference Resolution: Can LLMs Dethrone Traditional Approaches?**Michal Novák, Miloslav Konopik, Anna Nedoluzhko, et al.*2025 · 10 citations*](https://md.research.elamur.ai/free/en/papers/novak-2025-findings-fourth-shared-task-multilingual/)
70. [**Challenges to Evaluating the Generalization of Coreference Resolution Models: A Measurement Modeling Perspective**Ian Porada, Alexandra Olteanu, Kaheer Suleman, et al.*2024 · 10 citations*](https://md.research.elamur.ai/free/en/papers/porada-2024-challenges-evaluating-generalization-coreference-resolution/)
71. [**Contrastive Representation Learning for Cross-Document Coreference Resolution of Events and Entities**Benjamin Hsu, Graham Horwood*2022 · 10 citations*](https://md.research.elamur.ai/free/en/papers/hsu-2022-contrastive-representation-learning-cross-document/)
72. [**Linear Cross-document Event Coreference Resolution with X-AMR**Shafiuddin Rehan Ahmed, George Arthur Baker, Evi Judge, et al.*2024 · 9 citations*](https://md.research.elamur.ai/free/en/papers/ahmed-2024-linear-cross-document-event-coreference/)
73. [**When Your Language Model Cannot Even Do Determiners Right: Probing for Anti-Presuppositions and the Maximize Presupposition! Principle**Judith Sieker, Sina Zarrieß*2023 · 9 citations*](https://md.research.elamur.ai/free/en/papers/sieker-2023-when-your-language-model-cannot/)
74. [**A Controlled Reevaluation of Coreference Resolution Models**Ian Porada, Xiyuan Zou, Jackie Chi Kit Cheung*2024 · 7 citations*](https://md.research.elamur.ai/free/en/papers/porada-2024-controlled-reevaluation-coreference-resolution-models/)
75. [**Multimodal Cross-Document Event Coreference Resolution Using Linear Semantic Transfer and Mixed-Modality Ensembles**Abhijnan Nath, Huma Jamil, Shafiuddin Rehan Ahmed, et al.*2024 · 7 citations*](https://md.research.elamur.ai/free/en/papers/nath-2024-multimodal-cross-document-event-coreference/)
76. [**Are BabyLMs Deaf to Gricean Maxims? A Pragmatic Evaluation of Sample-efficient Language Models**Raha Askari, Sina Zarrieß, Özge Alacam, et al.*2025 · 4 citations*](https://md.research.elamur.ai/free/en/papers/askari-2025-are-babylms-deaf-gricean-maxims/)
77. [**PCR4ALL: A Comprehensive Evaluation Benchmark for Pronoun Coreference Resolution in English**Xinran Zhao, Hongming Zhang, Yangqiu Song*2022 · 3 citations*](https://md.research.elamur.ai/free/en/papers/zhao-2022-pcr4all-comprehensive-evaluation-benchmark-pronoun/)
78. [**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*](https://md.research.elamur.ai/free/en/papers/liu-2024-hallucinations-large-language/)
79. [**Towards Handling Verb Phrase Ellipsis in English-Hindi Machine Translation**Niyati Bafna, Dipti Sharma*2019*](https://md.research.elamur.ai/free/en/papers/bafna-2019-handling-verb-phrase-ellipsis-english/)

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