🧑‍🔬 Who cites Michael Saxon

156 citations of their works from 155 papers of our library (of the 149,439 whose bibliographies we read) · 28 with a page here, the most cited first · ← Michael Saxon

1. The Llama 3 Herd of ModelsarXiv:2407.21783Aaron Grattafiori, Abhimanyu Dubey, Abhinav Jauhri, et al.2024 · 7,682 citations
2. A Survey on In-context LearningarXiv:2301.00234Qingxiu Dong, Lei Li, Damai Dai, et al.2023 · 978 citations
3. LegalBench: A Collaboratively Built Benchmark for Measuring Legal Reasoning in Large Language ModelsarXiv:2308.11462Neel Guha; Julian Nyarko; Daniel E. Ho; Christopher Re; Adam Chilton; Aditya Narayana; Alex Chohlas-Wood; Austin Peters; Brandon Waldon; Daniel N. Rockmore; Diego Zambrano; Dmitry Talisman; Enam Hoque; Faiz Surani; Frank Fagan; Galit Sarfaty; Gregory M. Dickinson; Haggai Porat; Jason Hegland; Jessica Wu; Joe Nudell; Joel Niklaus; John Nay; Jonathan H. Choi; Kevin Tobia; Margaret Hagan; Megan Ma; Michael Livermore; Nikon Rasumov-Rahe; Nils Holzenberger; Noam Kolt; Peter Henderson; Sean Rehaag; Sharad Goel; Shang Gao; Spencer Williams; Sunny Gandhi; Tom Zur; Varun Iyer;, Zehua Li2023 · 389 citations
4. A Survey on Model Compression for Large Language ModelsTransactions of the Association for Computational Linguistics (TACL)Xunyu Zhu, Jian Li, Yong Liu, et al.2024 · 381 citations
5. Large Language Models for Data Annotation and Synthesis: A SurveyConference on Empirical Methods in Natural Language Processing (EMNLP)Zhen Tan, Dawei Li, Song Wang, et al.2024 · 262 citations
6. The Prompt Report: A Systematic Survey of Prompt Engineering TechniquesarXiv:2406.06608Sander Schulhoff, Michael Ilie, Nishant Balepur, et al.2024 · 189 citations
7. LESS: Selecting Influential Data for Targeted Instruction TuningarXiv:2402.04333Mengzhou Xia, Sadhika Malladi, Suchin Gururangan, et al.2024 · 178 citations
8. When Can LLMs Actually Correct Their Own Mistakes? A Critical Survey of Self-Correction of LLMsTransactions of the Association for Computational Linguistics (TACL)Ryo Kamoi, Yusen Zhang, Nan Zhang, et al.2024 · 161 citations
9. Many-Shot In-Context LearningConference on Neural Information Processing Systems (NeurIPS)Rishabh Agarwal, Avi Singh, Lei M. Zhang, et al.2024 · 142 citations
10. Automatically Correcting Large Language Models: Surveying the Landscape of Diverse Automated Correction StrategiesTransactions of the Association for Computational Linguistics (TACL)Liangming Pan, Michael Saxon, Wenda Xu, et al.2024 · 140 citations
11. A Survey on Data Selection for Language ModelsarXiv:2402.16827Alon Albalak, Yanai Elazar, Sang Michael Xie, et al.2024 · 95 citations
12. Testing the General Deductive Reasoning Capacity of Large Language Models Using OOD ExamplesConference on Neural Information Processing Systems (NeurIPS)Abulhair Saparov, Richard Yuanzhe Pang, Vishakh Padmakumar, et al.2023 · 84 citations
13. Large Language Model Instruction Following: A Survey of Progresses and ChallengesComputational LinguisticsRenze Lou, Kai Zhang, Wenpeng Yin2024 · 83 citations
14. Can LLMs Learn from Previous Mistakes? Investigating LLMs' Errors to Boost for ReasoningAnnual Meeting of the Association for Computational Linguistics (ACL)Yongqi Tong, Dawei Li, Sizhe Wang, et al.2024 · 57 citations
15. In-context Learning with Retrieved Demonstrations for Language Models: A SurveyarXiv:2401.11624Man Luo, Xin Xu, Yue Liu, et al.2024 · 54 citations
16. Which of These Best Describes Multiple Choice Evaluation with LLMs? A) Forced B) Flawed C) Fixable D) All of the AboveAnnual Meeting of the Association for Computational Linguistics (ACL)Nishant Balepur, Rachel Rudinger, Jordan Boyd-Graber2025 · 28 citations
17. MQM-APE: Toward High-Quality Error Annotation Predictors with Automatic Post-Editing in LLM Translation EvaluatorsInternational Conference on Computational Linguistics (COLING)Qingyu Lu, Liang Ding, Kanjian Zhang, et al.2025 · 22 citations
18. Understanding Emergent In-Context Learning from a Kernel Regression PerspectivearXiv:2305.12766Chi Han, Ziqi Wang, Han Zhao, et al.2023 · 21 citations
19. Please Translate Again: Two Simple Experiments on Whether Human-Like Reasoning Helps TranslationConference on Empirical Methods in Natural Language Processing (EMNLP)Di Wu, Seth Aycock, Christof Monz2025 · 12 citations
20. Confidence v.s. Critique: A Decomposition of Self-Correction Capability for LLMsAnnual Meeting of the Association for Computational Linguistics (ACL)Zhe Yang, Yichang Zhang, Yudong Wang, et al.2025 · 11 citations
21. Subjectivity Detection in English News using Large Language ModelsWorkshop on Computational Approaches to Subjectivity, Sentiment and Social Media AnalysisMohammad Shokri, Vivek Sharma, Elena Filatova, et al.2024 · 7 citations
22. Skill Discovery for Software Scripting Automation via Offline Simulations with LLMsFindings of the Association for Computational LinguisticsPaiheng Xu, Gang Wu, Xiang Chen, et al.2026 · 3 citations
23. Towards Controllable Natural Language Inference through Lexical Inference TypesarXiv:2308.03581Yingji Zhang, Danilo S. Carvalho, Ian Pratt-Hartmann, et al.2023 · 2 citations
24. CAIRE: Cultural Attribution of Images with RetrievalConference of the European Chapter of the Association for Computational Linguistics (EACL)Arnav Yayavaram, Siddharth Yayavaram, Simran Khanuja, et al.2026 · 1 citation
25. Cross-Lingual Emotion Recognition in Balinese Text using Multilingual-LLMs under Peer-Collaborations SettingsWorkshop on Language Models for Low-Resource LanguagesPutu Kussa Laksana Utama, Jilles Steeve Dibangoye, Tsegaye Misikir Tashu2026 · 1 citation
26. FactSelfCheck: Fact-Level Black-Box Hallucination Detection for LLMsFindings of the Association for Computational LinguisticsAlbert Sawczyn, Jakub Binkowski, Denis Janiak, et al.2026
27. ImageEval 2026: Culturally Grounded Arabic Multimodal EvaluationarXiv:2608.30475Samir Abdaljalil, Hunzalah Hassan Bhatti, Ahlam Bashiti, et al.2026
28. ReActR: Reasoning through Error-Activated Reflection for LLM Post-TrainingAnnual Meeting of the Association for Computational Linguistics (ACL)Lina Sun2026

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