🧑‍🔬 Who cites Haewoon Kwak

182 citations of their works from 180 papers of our library (of the 149,439 whose bibliographies we read) · 42 with a page here, the most cited first · ← Haewoon Kwak

1. Llama 2: Open Foundation and Fine-Tuned Chat ModelsarXiv:2307.09288Hugo Touvron, Louis Martin, Kevin Stone, et al.2023 · 9,669 citations
2. Judging LLM-as-a-Judge with MT-Bench and Chatbot ArenaConference on Neural Information Processing Systems (NeurIPS)Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, et al.2023 · 4,573 citations
3. ChatGPT Outperforms Crowd-Workers for Text-Annotation TasksProceedings of the National Academy of SciencesFabrizio Gilardi, Meysam Alizadeh, Maël Kubli2023 · 1,536 citations
4. Summary of ChatGPT-Related Research and Perspective Towards the Future of Large Language ModelsarXiv:2304.01852 · Meta-Radiology (2023)100,017Yiheng Liu, Tianle Han, Siyuan Ma, et al.2023 · 940 citations
5. Gender and Representation Bias in GPT-3 Generated StoriesWorkshop on Narrative UnderstandingLi Lucy, David Bamman2021 · 387 citations
6. Is ChatGPT a Good NLG Evaluator? A Preliminary StudyarXiv:2303.04048Jiaan Wang, Yunlong Liang, Fandong Meng, et al.2023 · 287 citations
7. TrustLLM: Trustworthiness in Large Language ModelsarXiv:2401.05561Yue Huang, Lichao Sun, Haoran Wang, et al.2024 · 237 citations
8. MOMENTA: A Multimodal Framework for Detecting Harmful Memes and Their TargetsFindings of the Association for Computational LinguisticsShraman Pramanick, Shivam Sharma, Dimitar Dimitrov, et al.2021 · 212 citations
9. A Systematic Study and Comprehensive Evaluation of ChatGPT on Benchmark DatasetsFindings of the Association for Computational LinguisticsMd Tahmid Rahman Laskar, M Saiful Bari, Mizanur Rahman, et al.2023 · 200 citations
10. We Can Detect Your Bias: Predicting the Political Ideology of News ArticlesConference on Empirical Methods in Natural Language Processing (EMNLP)Ramy Baly, Giovanni Da San Martino, James Glass, et al.2020 · 199 citations
11. A Survey on Stance Detection for Mis- and Disinformation IdentificationFindings of the Association for Computational LinguisticsMomchil Hardalov, Arnav Arora, Preslav Nakov, et al.2022 · 188 citations
12. LawBench: Benchmarking Legal Knowledge of Large Language ModelsarXiv:2309.16289Zhiwei Fei, Xiaoyu Shen, Dawei Zhu, et al.2023 · 186 citations
13. Evaluating Large Language Models: A Comprehensive SurveyarXiv:2310.19736Zishan Guo, Renren Jin, Chuang Liu, et al.2023 · 182 citations
14. Learning to Compress Prompts with Gist TokensConference on Neural Information Processing Systems (NeurIPS)Jesse Mu, Xiang Lisa Li, Noah Goodman2023 · 178 citations
15. Large Language Models Effectively Leverage Document-level Context for Literary Translation, but Critical Errors PersistConference on Machine Translation (WMT)Marzena Karpinska, Mohit Iyyer2023 · 156 citations
16. Investigating Data Contamination in Modern Benchmarks for Large Language ModelsConference of the North American Chapter of the Association for Computational Linguistics (NAACL)Chunyuan Deng, Yilun Zhao, Xiangru Tang, et al.2024 · 137 citations
17. Best Practices and Lessons Learned on Synthetic DataarXiv:2404.07503Ruibo Liu, Jerry Wei, Fangyu Liu, et al.2024 · 119 citations
18. TrueTeacher: Learning Factual Consistency Evaluation with Large Language ModelsConference on Empirical Methods in Natural Language Processing (EMNLP)Zorik Gekhman, Jonathan Herzig, Roee Aharoni, et al.2023 · 106 citations
19. CoAnnotating: Uncertainty-Guided Work Allocation between Human and Large Language Models for Data AnnotationConference on Empirical Methods in Natural Language Processing (EMNLP)Minzhi Li, Taiwei Shi, Caleb Ziems, et al.2023 · 101 citations
20. ChatGPT: Beginning of an End of Manual Linguistic Data Annotation? Use Case of Automatic Genre IdentificationarXiv:2303.03953Taja Kuzman, Igor Mozetič, Nikola Ljubešic2023 · 95 citations
21. FollowBench: A Multi-level Fine-grained Constraints Following Benchmark for Large Language ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Yuxin Jiang, Yufei Wang, Xingshan Zeng, et al.2024 · 86 citations
22. GPTEval: A Survey on Assessments of ChatGPT and GPT-4Language Resources and Evaluation Conference (LREC)Rui Mao, Guanyi Chen, Xulang Zhang, et al.2024 · 75 citations
23. Infusing Knowledge from Wikipedia to Enhance Stance DetectionWorkshop on Computational Approaches to Subjectivity, Sentiment and Social Media AnalysisZihao He, Negar Mokhberian, Kristina Lerman2022 · 72 citations
24. Leveraging Large Language Models for NLG Evaluation: Advances and ChallengesConference on Empirical Methods in Natural Language Processing (EMNLP)Zhen Li, Xiaohan Xu, Tao Shen, et al.2024 · 63 citations
25. Shepherd: A Critic for Language Model GenerationarXiv:2308.04592Tianlu Wang, Ping Yu, Xiaoqing Ellen Tan, et al.2023 · 60 citations
26. The Effectiveness of LLMs as Annotators: A Comparative Overview and Empirical Analysis of Direct RepresentationLanguage Resources and Evaluation Conference (LREC)Maja Pavlovic, Massimo Poesio2024 · 49 citations
27. Automated stance detection in complex topics and small languages: the challenging case of immigration in polarizing news mediaPLoS ONEMark Mets, Andres Karjus, Indrek Ibrus, et al.2023 · 43 citations
28. A Second Pandemic? Analysis of Fake News about COVID-19 Vaccines in QatarRecent Advances in Natural Language Processing (RANLP)Preslav Nakov, Firoj Alam, Shaden Shaar, et al.2021 · 33 citations
29. AutomaTikZ: Text-Guided Synthesis of Scientific Vector Graphics with TikZInternational Conference on Learning Representations (ICLR)Jonas Belouadi, Anne Lauscher, Steffen Eger2023 · 31 citations
30. The FIGNEWS Shared Task on News Media NarrativesArabic Natural Language Processing Conference (ArabicNLP)Wajdi Zaghouani, Mustafa Jarrar, Nizar Habash, et al.2024 · 26 citations
31. CofiPara: A Coarse-to-fine Paradigm for Multimodal Sarcasm Target Identification with Large Multimodal ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Zixin Chen, Hongzhan Lin, Ziyang Luo, et al.2024 · 20 citations
32. Prompting and Fine-Tuning Open-Sourced Large Language Models for Stance ClassificationarXiv:2309.13734Iain J. Cruickshank, Lynnette Hui Xian Ng2023 · 18 citations
33. Evolving linguistic divergence on polarizing social mediaHumanities and Social Sciences CommunicationsAndres Karjus, Christine Cuskley2023 · 16 citations
34. MGKM at StanceEval2024 Fine-Tuning Large Language Models for Arabic Stance DetectionArabic Natural Language Processing Conference (ArabicNLP)Mamoun Alghaslan, Khaled Almutairy2024 · 7 citations
35. State of the Art in Text Classification for South Slavic Languages: Fine-Tuning or Prompting?Shaping Multilingual, Multimodal AI for the Social Sciences and Humanities @ LRECTaja Kuzman Pungeršek, Peter Rupnik, Ivan Porupski, et al.2026 · 2 citations
36. ArTrivia: Harvesting Arabic Wikipedia to Build A New Arabic Question Answering DatasetArabic Natural Language Processing Conference (ArabicNLP)Sultan Alrowili, K Vijay-Shanker2023 · 2 citations
37. Can a Large Language Model Replace Humans at Rating Lexical Semantic Relations Strength?Computational LinguisticsAndré Fernandes dos Santos, José Paulo Leal2026 · 1 citation
38. Evaluating Machine Translation Datasets for Low-Web Data Languages: A Gendered LensFindings of the Association for Computational LinguisticsHellina Hailu Nigatu, Bethelhem Yemane Mamo, Bontu Fufa Balcha, et al.2026 · 1 citation
39. The Hidden Language of Harm: Examining the Role of Emojis in Harmful Online Communication and Content ModerationWorkshop on Natural Language Processing and Computational Social ScienceYuhang Zhou, Yimin Xiao, Wei Ai, et al.2026 · 1 citation
40. A Multi-View Media Profiling Suite: Resources, Evaluation, and AnalysisFindings of the Association for Computational LinguisticsMuhammad Arslan Manzoor, Dilshod Azizov, Daniil Orel, et al.2026
41. Assessing Web Search Credibility and Response Groundedness in Chat AssistantsConference of the European Chapter of the Association for Computational Linguistics (EACL)Ivan Vykopal, Matúš Pikuliak, Simon Ostermann, et al.2026
42. Identifying the Achilles' Heel: An Iterative Method for Uncovering Factual Errors in Large Language ModelsFindings of the Association for Computational LinguisticsWenxuan Wang, Yuk-Kit Chan, Zixuan Ling, et al.2026

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