---
title: "Who cites Jisun An — 180 papers of our library"
description: "180 papers of the Elamur Research library cite the works of Jisun An, found in their bibliographies; the most cited first."
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---
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# Who cites Jisun An

182 citations of their works from 180 papers of our library (of the 149,439 whose bibliographies we read) · 47 with a page here, the most cited first · [← Jisun An](https://md.research.elamur.ai/free/en/authors/jisun-an/)

1. [**Llama 2: Open Foundation and Fine-Tuned Chat Models arXiv:2307.09288**Hugo Touvron, Louis Martin, Kevin Stone, et al.*2023 · 9,669 citations*](https://md.research.elamur.ai/free/en/papers/touvron-2023-llama-2-open-foundation-fine/)
2. **[Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://md.research.elamur.ai/free/en/papers/zheng-2023-judging-llm-judge-mt-bench/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, et al.*2023 · 4,573 citations*
3. **[ChatGPT Outperforms Crowd-Workers for Text-Annotation Tasks](https://md.research.elamur.ai/free/en/papers/gilardi-2023-chatgpt-outperforms-crowd-workers-text/) [Proceedings of the National Academy of Sciences](https://md.research.elamur.ai/free/en/venues/proceedings-of-the-national-academy-of-sciences/)**Fabrizio Gilardi, Meysam Alizadeh, Maël Kubli*2023 · 1,536 citations*
4. [**Summary of ChatGPT-Related Research and Perspective Towards the Future of Large Language Models arXiv:2304.01852 · Meta-Radiology (2023)100,017**Yiheng Liu, Tianle Han, Siyuan Ma, et al.*2023 · 940 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-summary-chatgpt-related-research-perspective/)
5. **[Gender and Representation Bias in GPT-3 Generated Stories](https://md.research.elamur.ai/free/en/papers/lucy-2021-gender-representation-bias-gpt-3/) [Workshop on Narrative Understanding](https://md.research.elamur.ai/free/en/venues/nuse/)**Li Lucy, David Bamman*2021 · 387 citations*
6. [**Is ChatGPT a Good NLG Evaluator? A Preliminary Study arXiv:2303.04048**Jiaan Wang, Yunlong Liang, Fandong Meng, et al.*2023 · 287 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-chatgpt-good-nlg-evaluator-preliminary/)
7. [**TrustLLM: Trustworthiness in Large Language Models arXiv:2401.05561**Yue Huang, Lichao Sun, Haoran Wang, et al.*2024 · 237 citations*](https://md.research.elamur.ai/free/en/papers/huang-2024-trustllm-trustworthiness-large-language-models/)
8. **[MOMENTA: A Multimodal Framework for Detecting Harmful Memes and Their Targets](https://md.research.elamur.ai/free/en/papers/pramanick-2021-momenta-multimodal-framework-detecting-harmful/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Shraman Pramanick, Shivam Sharma, Dimitar Dimitrov, et al.*2021 · 212 citations*
9. **[A Systematic Study and Comprehensive Evaluation of ChatGPT on Benchmark Datasets](https://md.research.elamur.ai/free/en/papers/laskar-2023-systematic-study-comprehensive-evaluation-chatgpt/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Md Tahmid Rahman Laskar, M Saiful Bari, Mizanur Rahman, et al.*2023 · 200 citations*
10. **[A Survey of Safety and Trustworthiness of Large Language Models through the Lens of Verification and Validation](https://md.research.elamur.ai/free/en/papers/huang-2023-survey-safety-trustworthiness-large-language/) [Artificial Intelligence Review](https://md.research.elamur.ai/free/en/venues/artificial-intelligence-review/)**Xiaowei Huang, Wenjie Ruan, Wei Huang, et al.*2023 · 199 citations*
11. **[We Can Detect Your Bias: Predicting the Political Ideology of News Articles](https://md.research.elamur.ai/free/en/papers/baly-2020-we-can-detect-your-bias/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Ramy Baly, Giovanni Da San Martino, James Glass, et al.*2020 · 199 citations*
12. [**LawBench: Benchmarking Legal Knowledge of Large Language Models arXiv:2309.16289**Zhiwei Fei, Xiaoyu Shen, Dawei Zhu, et al.*2023 · 188 citations*](https://md.research.elamur.ai/free/en/papers/fei-2023-lawbench-benchmarking-legal-knowledge-large/)
13. **[A Survey on Stance Detection for Mis- and Disinformation Identification](https://md.research.elamur.ai/free/en/papers/hardalov-2022-survey-stance-detection-mis-disinformation/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Momchil Hardalov, Arnav Arora, Preslav Nakov, et al.*2022 · 188 citations*
14. [**Evaluating Large Language Models: A Comprehensive Survey arXiv:2310.19736**Zishan Guo, Renren Jin, Chuang Liu, et al.*2023 · 182 citations*](https://md.research.elamur.ai/free/en/papers/guo-2023-evaluating-large-language-models-comprehensive/)
15. **[Learning to Compress Prompts with Gist Tokens](https://md.research.elamur.ai/free/en/papers/mu-2023-learning-compress-prompts-gist-tokens/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Jesse Mu, Xiang Lisa Li, Noah Goodman*2023 · 178 citations*
16. **[Large Language Models Effectively Leverage Document-level Context for Literary Translation, but Critical Errors Persist](https://md.research.elamur.ai/free/en/papers/karpinska-2023-large-language-models-effectively-leverage/) [Conference on Machine Translation (WMT)](https://md.research.elamur.ai/free/en/venues/wmt/)**Marzena Karpinska, Mohit Iyyer*2023 · 156 citations*
17. [**Evaluating ChatGPT's Information Extraction Capabilities: An Assessment of Performance, Explainability, Calibration, and Faithfulness arXiv:2304.11633**Bo Li, Gexiang Fang, Yang Yang, et al.*2023 · 142 citations*](https://md.research.elamur.ai/free/en/papers/li-2023-evaluating-chatgpt-s-information-extraction/)
18. **[Investigating Data Contamination in Modern Benchmarks for Large Language Models](https://md.research.elamur.ai/free/en/papers/deng-2024-investigating-data-contamination-modern-benchmarks/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Chunyuan Deng, Yilun Zhao, Xiangru Tang, et al.*2024 · 137 citations*
19. [**Best Practices and Lessons Learned on Synthetic Data arXiv:2404.07503**Ruibo Liu, Jerry Wei, Fangyu Liu, et al.*2024 · 119 citations*](https://md.research.elamur.ai/free/en/papers/liu-2024-best-practices-lessons-learned-synthetic/)
20. [**Don't Make Your LLM an Evaluation Benchmark Cheater arXiv:2311.01964**Kun Zhou, Yutao Zhu, Zhipeng Chen, et al.*2023 · 112 citations*](https://md.research.elamur.ai/free/en/papers/zhou-2023-don-t-make-llm-evaluation/)
21. **[TrueTeacher: Learning Factual Consistency Evaluation with Large Language Models](https://md.research.elamur.ai/free/en/papers/gekhman-2023-trueteacher-learning-factual-consistency-evaluation/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Zorik Gekhman, Jonathan Herzig, Roee Aharoni, et al.*2023 · 106 citations*
22. **[CoAnnotating: Uncertainty-Guided Work Allocation between Human and Large Language Models for Data Annotation](https://md.research.elamur.ai/free/en/papers/li-2023-coannotating-uncertainty-guided-work-allocation/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Minzhi Li, Taiwei Shi, Caleb Ziems, et al.*2023 · 101 citations*
23. [**ChatGPT: Beginning of an End of Manual Linguistic Data Annotation? Use Case of Automatic Genre Identification arXiv:2303.03953**Taja Kuzman, Igor Mozetič, Nikola Ljubešic*2023 · 95 citations*](https://md.research.elamur.ai/free/en/papers/kuzman-2023-chatgpt-beginning-end-manual-linguistic/)
24. **[Task Contamination: Language Models May Not Be Few-Shot Anymore](https://md.research.elamur.ai/free/en/papers/li-2023-task-contamination-language-models-may/) [Proceedings of the AAAI Conference on Artificial Intelligence](https://md.research.elamur.ai/free/en/venues/proceedings-of-the-aaai-conference-on-artificial-intelligence/)**Changmao Li, Jeffrey Flanigan*2023 · 89 citations*
25. **[FollowBench: A Multi-level Fine-grained Constraints Following Benchmark for Large Language Models](https://md.research.elamur.ai/free/en/papers/jiang-2024-followbench-multi-level-fine-grained/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yuxin Jiang, Yufei Wang, Xingshan Zeng, et al.*2024 · 86 citations*
26. [**OpenMathInstruct-2: Accelerating AI for Math with Massive Open-Source Instruction Data arXiv:2410.01560**Shubham Toshniwal, Wei Du, Ivan Moshkov, et al.*2024 · 85 citations*](https://md.research.elamur.ai/free/en/papers/toshniwal-2024-openmathinstruct-2-accelerating-ai-math/)
27. **[GPTEval: A Survey on Assessments of ChatGPT and GPT-4](https://md.research.elamur.ai/free/en/papers/mao-2024-gpteval-survey-assessments-chatgpt-gpt/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Rui Mao, Guanyi Chen, Xulang Zhang, et al.*2024 · 75 citations*
28. **[Infusing Knowledge from Wikipedia to Enhance Stance Detection](https://md.research.elamur.ai/free/en/papers/he-2022-infusing-knowledge-wikipedia-enhance-stance/) [Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis](https://md.research.elamur.ai/free/en/venues/wassa/)**Zihao He, Negar Mokhberian, Kristina Lerman*2022 · 72 citations*
29. **[Leveraging Large Language Models for NLG Evaluation: Advances and Challenges](https://md.research.elamur.ai/free/en/papers/li-2024-leveraging-large-language-models-nlg/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Zhen Li, Xiaohan Xu, Tao Shen, et al.*2024 · 63 citations*
30. [**Shepherd: A Critic for Language Model Generation arXiv:2308.04592**Tianlu Wang, Ping Yu, Xiaoqing Ellen Tan, et al.*2023 · 60 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-shepherd-critic-language-model-generation/)
31. **[The Effectiveness of LLMs as Annotators: A Comparative Overview and Empirical Analysis of Direct Representation](https://md.research.elamur.ai/free/en/papers/pavlovic-2024-effectiveness-llms-annotators-comparative-overview/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Maja Pavlovic, Massimo Poesio*2024 · 49 citations*
32. **[Automated stance detection in complex topics and small languages: the challenging case of immigration in polarizing news media](https://md.research.elamur.ai/free/en/papers/mets-2023-automated-stance-detection-complex-topics/) [PLoS ONE](https://md.research.elamur.ai/free/en/venues/plos-one/)**Mark Mets, Andres Karjus, Indrek Ibrus, et al.*2023 · 43 citations*
33. **[A Second Pandemic? Analysis of Fake News about COVID-19 Vaccines in Qatar](https://md.research.elamur.ai/free/en/papers/nakov-2021-second-pandemic-analysis-fake-news/) [Recent Advances in Natural Language Processing (RANLP)](https://md.research.elamur.ai/free/en/venues/ranlp/)**Preslav Nakov, Firoj Alam, Shaden Shaar, et al.*2021 · 33 citations*
34. **[AutomaTikZ: Text-Guided Synthesis of Scientific Vector Graphics with TikZ](https://md.research.elamur.ai/free/en/papers/belouadi-2023-automatikz-text-guided-synthesis-scientific/) [International Conference on Learning Representations (ICLR)](https://md.research.elamur.ai/free/en/venues/international-conference-on-learning-representations-iclr/)**Jonas Belouadi, Anne Lauscher, Steffen Eger*2023 · 31 citations*
35. **[The FIGNEWS Shared Task on News Media Narratives](https://md.research.elamur.ai/free/en/papers/zaghouani-2024-fignews-shared-task-news-media/) [Arabic Natural Language Processing Conference (ArabicNLP)](https://md.research.elamur.ai/free/en/venues/arabicnlp/)**Wajdi Zaghouani, Mustafa Jarrar, Nizar Habash, et al.*2024 · 26 citations*
36. **[CofiPara: A Coarse-to-fine Paradigm for Multimodal Sarcasm Target Identification with Large Multimodal Models](https://md.research.elamur.ai/free/en/papers/chen-2024-cofipara-coarse-fine-paradigm-multimodal/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zixin Chen, Hongzhan Lin, Ziyang Luo, et al.*2024 · 20 citations*
37. [**Prompting and Fine-Tuning Open-Sourced Large Language Models for Stance Classification arXiv:2309.13734**Iain J. Cruickshank, Lynnette Hui Xian Ng*2023 · 18 citations*](https://md.research.elamur.ai/free/en/papers/cruickshank-2023-prompting-fine-tuning-open-sourced/)
38. **[Evolving linguistic divergence on polarizing social media](https://md.research.elamur.ai/free/en/papers/karjus-2023-evolving-linguistic-divergence-polarizing-social/) [Humanities and Social Sciences Communications](https://md.research.elamur.ai/free/en/venues/humanities-and-social-sciences-communications/)**Andres Karjus, Christine Cuskley*2023 · 16 citations*
39. **[MGKM at StanceEval2024 Fine-Tuning Large Language Models for Arabic Stance Detection](https://md.research.elamur.ai/free/en/papers/alghaslan-2024-mgkm-stanceeval2024-fine-tuning-large/) [Arabic Natural Language Processing Conference (ArabicNLP)](https://md.research.elamur.ai/free/en/venues/arabicnlp/)**Mamoun Alghaslan, Khaled Almutairy*2024 · 7 citations*
40. **[State of the Art in Text Classification for South Slavic Languages: Fine-Tuning or Prompting?](https://md.research.elamur.ai/free/en/papers/pungersek-2026-state-art-text-classification-south/) [Shaping Multilingual, Multimodal AI for the Social Sciences and Humanities @ LREC](https://md.research.elamur.ai/free/en/venues/llms4ssh/)**Taja Kuzman Pungeršek, Peter Rupnik, Ivan Porupski, et al.*2026 · 2 citations*
41. **[ArTrivia: Harvesting Arabic Wikipedia to Build A New Arabic Question Answering Dataset](https://md.research.elamur.ai/free/en/papers/alrowili-2023-artrivia-harvesting-arabic-wikipedia-build/) [Arabic Natural Language Processing Conference (ArabicNLP)](https://md.research.elamur.ai/free/en/venues/arabicnlp/)**Sultan Alrowili, K Vijay-Shanker*2023 · 2 citations*
42. **[Can a Large Language Model Replace Humans at Rating Lexical Semantic Relations Strength?](https://md.research.elamur.ai/free/en/papers/santos-2026-can-large-language-model-replace/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**André Fernandes dos Santos, José Paulo Leal*2026 · 1 citation*
43. **[Evaluating Machine Translation Datasets for Low-Web Data Languages: A Gendered Lens](https://md.research.elamur.ai/free/en/papers/nigatu-2026-evaluating-machine-translation-datasets-low/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Hellina Hailu Nigatu, Bethelhem Yemane Mamo, Bontu Fufa Balcha, et al.*2026 · 1 citation*
44. **[The Hidden Language of Harm: Examining the Role of Emojis in Harmful Online Communication and Content Moderation](https://md.research.elamur.ai/free/en/papers/zhou-2026-hidden-language-harm-examining-role/) [Workshop on Natural Language Processing and Computational Social Science](https://md.research.elamur.ai/free/en/venues/nlpcss/)**Yuhang Zhou, Yimin Xiao, Wei Ai, et al.*2026 · 1 citation*
45. **[A Multi-View Media Profiling Suite: Resources, Evaluation, and Analysis](https://md.research.elamur.ai/free/en/papers/manzoor-2026-multi-view-media-profiling-suite/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Muhammad Arslan Manzoor, Dilshod Azizov, Daniil Orel, et al.*2026*
46. **[Assessing Web Search Credibility and Response Groundedness in Chat Assistants](https://md.research.elamur.ai/free/en/papers/vykopal-2026-assessing-web-search-credibility-response/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Ivan Vykopal, Matúš Pikuliak, Simon Ostermann, et al.*2026*
47. **[Identifying the Achilles' Heel: An Iterative Method for Uncovering Factual Errors in Large Language Models](https://md.research.elamur.ai/free/en/papers/wang-2026-identifying-achilles-heel-iterative-method/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Wenxuan Wang, Yuk-Kit Chan, Zixuan Ling, et al.*2026*

133 more citing papers of our library have no page here yet.

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