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Who cites Ryan Teehan

2,294 citations of their works from 2,175 papers of our library (of the 149,439 whose bibliographies we read) · 628 with a page here, the most cited first · page 4 of 7 · 301–400 · ← Ryan Teehan

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  1. SeaEval for Multilingual Foundation Models: From Cross-Lingual Alignment to Cultural ReasoningConference of the North American Chapter of the Association for Computational Linguistics (NAACL)Bin Wang, Zhengyuan Liu, Xin Huang, et al.2024 · 85 citations
  2. Investigating the Translation Performance of a Large Multilingual Language Model: the Case of BLOOMarXiv:2303.01911Rachel Bawden, François Yvon2023 · 85 citations
  3. AlexaTM 20B: Few-Shot Learning Using a Large-Scale Multilingual Seq2Seq ModelarXiv:2208.01448Saleh Soltan, Shankar Ananthakrishnan, Jack FitzGerald, et al.2022 · 84 citations
  4. Mitigating the Alignment Tax of RLHFConference on Empirical Methods in Natural Language Processing (EMNLP)Yong Lin, Hangyu Lin, Wei Xiong, et al.2024 · 83 citations
  5. DOC: Improving Long Story Coherence With Detailed Outline ControlAnnual Meeting of the Association for Computational Linguistics (ACL)Kevin Yang, Dan Klein, Nanyun Peng, et al.2023 · 83 citations
  6. ORCA: A Challenging Benchmark for Arabic Language UnderstandingFindings of the Association for Computational LinguisticsAbdelRahim Elmadany, ElMoatez Billah Nagoudi, Muhammad Abdul-Mageed2023 · 83 citations
  7. Survey of Vulnerabilities in Large Language Models Revealed by Adversarial AttacksarXiv:2310.10844Erfan Shayegani, Md Abdullah Al Mamun, Yu Fu, et al.2023 · 83 citations
  8. Knowledge Fusion of Large Language ModelsarXiv:2401.10491Fanqi Wan, Xinting Huang, Deng Cai, et al.2024 · 82 citations
  9. Smaug: Fixing Failure Modes of Preference Optimisation with DPO-PositivearXiv:2402.13228Arka Pal, Deep Karkhanis, Samuel Dooley, et al.2024 · 82 citations
  10. Style Over Substance: Evaluation Biases for Large Language ModelsInternational Conference on Computational Linguistics (COLING)Minghao Wu, Alham Fikri Aji2025 · 81 citations
  11. You Only Look at Screens: Multimodal Chain-of-Action AgentsFindings of the Association for Computational LinguisticsZhuosheng Zhang, Aston Zhang2024 · 81 citations
  12. MasakhaNEWS: News Topic Classification for African languagesInternational Joint Conference on Natural Language Processing (IJCNLP)David Ifeoluwa Adelani, Marek Masiak, Israel Abebe Azime, et al.2023 · 81 citations
  13. AppWorld: A Controllable World of Apps and People for Benchmarking Interactive Coding AgentsAnnual Meeting of the Association for Computational Linguistics (ACL)Harsh Trivedi, Tushar Khot, Mareike Hartmann, et al.2024 · 80 citations
  14. KoBBQ: Korean Bias Benchmark for Question AnsweringTransactions of the Association for Computational Linguistics (TACL)Jiho Jin, Jiseon Kim, Nayeon Lee, et al.2024 · 78 citations
  15. Multilingual Instruction Tuning With Just a Pinch of MultilingualityFindings of the Association for Computational LinguisticsUri Shaham, Jonathan Herzig, Roee Aharoni, et al.2024 · 78 citations
  16. KMMLU: Measuring Massive Multitask Language Understanding in KoreanConference of the North American Chapter of the Association for Computational Linguistics (NAACL)Guijin Son, Hanwool Lee, Sungdong Kim, et al.2025 · 76 citations
  17. Domain Adaptation with Pre-trained Transformers for Query-Focused Abstractive Text SummarizationComputational LinguisticsMd Tahmid Rahman Laskar, Enamul Hoque, Jimmy Xiangji Huang2022 · 76 citations
  18. PUB: A Pragmatics Understanding Benchmark for Assessing LLMs' Pragmatics CapabilitiesSettaluri Sravanthi, Meet Doshi, Pavan Tankala, et al.2024 · 75 citations
  19. SciCode: A Research Coding Benchmark Curated by ScientistsConference on Neural Information Processing Systems (NeurIPS)Minyang Tian, Luyu Gao, Shizhuo Dylan Zhang, et al.2024 · 75 citations
  20. Harnessing Explanations: LLM-to-LM Interpreter for Enhanced Text-Attributed Graph Representation LearningarXiv:2305.19523Xiaoxin He, Xavier Bresson, Thomas Laurent, et al.2023 · 75 citations
  21. Macaw-LLM: Multi-Modal Language Modeling with Image, Audio, Video, and Text IntegrationarXiv:2306.09093Chenyang Lyu, Minghao Wu, Longyue Wang, et al.2023 · 75 citations
  22. Training Language Models to Reason EfficientlyConference on Neural Information Processing Systems (NeurIPS)Daman Arora, Andrea Zanette2025 · 74 citations
  23. INCLUDE: Evaluating Multilingual Language Understanding with Regional KnowledgearXiv:2411.19799Angelika Romanou, Negar Foroutan, Anna Sotnikova, et al.2024 · 74 citations
  24. MediQ: Question-Asking LLMs and a Benchmark for Reliable Interactive Clinical ReasoningConference on Neural Information Processing Systems (NeurIPS)Shuyue Stella Li, Vidhisha Balachandran, Shangbin Feng, et al.2024 · 74 citations
  25. MEGAVERSE: Benchmarking Large Language Models Across Languages, Modalities, Models and TasksConference of the North American Chapter of the Association for Computational Linguistics (NAACL)Sanchit Ahuja, Divyanshu Aggarwal, Varun Gumma, et al.2024 · 74 citations
  26. When Benchmarks are Targets: Revealing the Sensitivity of Large Language Model LeaderboardsarXiv:2402.01781Norah Alzahrani, Hisham Abdullah Alyahya, Yazeed Alnumay, et al.2024 · 74 citations
  27. AlignBench: Benchmarking Chinese Alignment of Large Language ModelsarXiv:2311.18743Xiao Liu, Xuanyu Lei, Shengyuan Wang, et al.2023 · 74 citations
  28. Large Language Models as Analogical ReasonersarXiv:2310.01714Michihiro Yasunaga, Xinyun Chen, Yujia Li, et al.2023 · 74 citations
  29. Benchmarking Benchmark Leakage in Large Language ModelsarXiv:2404.18824Ruijie Xu, Zengzhi Wang, Run-Ze Fan, et al.2024 · 73 citations
  30. What Language Model to Train if You Have One Million GPU Hours?arXiv:2210.15424Teven Le Scao, Thomas Wang, Daniel Hesslow, et al.2022 · 73 citations
  31. InPars: Data Augmentation for Information Retrieval using Large Language ModelsarXiv:2202.05144Luiz Bonifacio, Hugo Abonizio, Marzieh Fadaee, et al.2022 · 72 citations
  32. Lion: Adversarial Distillation of Proprietary Large Language ModelsConference on Empirical Methods in Natural Language Processing (EMNLP)Yuxin Jiang, Chunkit Chan, Mingyang Chen, et al.2023 · 70 citations
  33. Mixture-of-Experts Meets Instruction Tuning:A Winning Combination for Large Language ModelsarXiv:2305.14705Sheng Shen, Le Hou, Yanqi Zhou, et al.2023 · 70 citations
  34. Automatic Generation of Socratic Subquestions for Teaching Math Word ProblemsConference on Empirical Methods in Natural Language Processing (EMNLP)Kumar Shridhar, Jakub Macina, Mennatallah El-Assady, et al.2022 · 70 citations
  35. Self-Alignment for Factuality: Mitigating Hallucinations in LLMs via Self-EvaluationAnnual Meeting of the Association for Computational Linguistics (ACL)Xiaoying Zhang, Baolin Peng, Ye Tian, et al.2024 · 69 citations
  36. Pushing Mixture of Experts to the Limit: Extremely Parameter Efficient MoE for Instruction TuningarXiv:2309.05444Ted Zadouri, Ahmet Ustun, Arash Ahmadian, et al.2023 · 69 citations
  37. A Thorough Examination of Decoding Methods in the Era of LLMsConference on Empirical Methods in Natural Language Processing (EMNLP)Chufan Shi, Haoran Yang, Deng Cai, et al.2024 · 68 citations
  38. ThoughtSource: A central hub for large language model reasoning dataScientific DataSimon Ott, Konstantin Hebenstreit, Valentin Lievin, et al.2023 · 68 citations
  39. Buffer of Thoughts: Thought-Augmented Reasoning with Large Language ModelsConference on Neural Information Processing Systems (NeurIPS)Ling Yang, Zhaochen Yu, Tianjun Zhang, et al.2024 · 67 citations
  40. Do Language Models Have a Common Sense regarding Time? Revisiting Temporal Commonsense Reasoning in the Era of Large Language ModelsConference on Empirical Methods in Natural Language Processing (EMNLP)Raghav Jain, Daivik Sojitra, Arkadeep Acharya, et al.2023 · 67 citations
  41. Learning by Distilling ContextarXiv:2209.15189Charlie Snell, Dan Klein, Ruiqi Zhong2022 · 67 citations
  42. Knowledge of Knowledge: Exploring Known-Unknowns Uncertainty with Large Language ModelsFindings of the Association for Computational LinguisticsAlfonso Amayuelas, Kyle Wong, Liangming Pan, et al.2024 · 66 citations
  43. An Open Source Data Contamination Report for Large Language ModelsarXiv:2310.17589Yucheng Li, Frank Guerin, Chenghua Lin2023 · 66 citations
  44. ARB: Advanced Reasoning Benchmark for Large Language ModelsarXiv:2307.13692Tomohiro Sawada, Daniel Paleka, Alexander Havrilla, et al.2023 · 66 citations
  45. MAIN-RAG: Multi-Agent Filtering Retrieval-Augmented GenerationAnnual Meeting of the Association for Computational Linguistics (ACL)Chia-Yuan Chang, Zhimeng Jiang, Vineeth Rakesh, et al.2025 · 65 citations
  46. Synthetic Data (Almost) from Scratch: Generalized Instruction Tuning for Language ModelsarXiv:2402.13064Haoran Li, Qingxiu Dong, Zhengyang Tang, et al.2024 · 65 citations
  47. Goat: Fine-tuned LLaMA Outperforms GPT-4 on Arithmetic TasksarXiv:2305.14201Tiedong Liu, Bryan Kian Hsiang Low2023 · 65 citations
  48. Questions Are All You Need to Train a Dense Passage RetrieverTransactions of the Association for Computational Linguistics (TACL)Devendra Singh Sachan, Mike Lewis, Dani Yogatama, et al.2023 · 65 citations
  49. Why think step by step? Reasoning emerges from the locality of experienceConference on Neural Information Processing Systems (NeurIPS)Ben Prystawski, Michael Y. Li, Noah D. Goodman2023 · 65 citations
  50. Meta-Rewarding Language Models: Self-Improving Alignment with LLM-as-a-Meta-JudgeConference on Empirical Methods in Natural Language Processing (EMNLP)Tianhao Wu, Weizhe Yuan, Olga Golovneva, et al.2025 · 64 citations
  51. A Peek into Token Bias: Large Language Models Are Not Yet Genuine ReasonersConference on Empirical Methods in Natural Language Processing (EMNLP)Bowen Jiang, Yangxinyu Xie, Zhuoqun Hao, et al.2024 · 64 citations
  52. Agent-FLAN: Designing Data and Methods of Effective Agent Tuning for Large Language ModelsFindings of the Association for Computational LinguisticsZehui Chen, Kuikun Liu, Qiuchen Wang, et al.2024 · 64 citations
  53. CValues: Measuring the Values of Chinese Large Language Models from Safety to ResponsibilityarXiv:2307.09705Guohai Xu, Jiayi Liu, Ming Yan, et al.2023 · 64 citations
  54. 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
  55. Exploring the Benefits of Training Expert Language Models over Instruction TuningarXiv:2302.03202Joel Jang, Seungone Kim, Seonghyeon Ye, et al.2023 · 63 citations
  56. Large Language Model Routing with Benchmark DatasetsarXiv:2309.15789Tal Shnitzer, Anthony Ou, Mirian Silva, et al.2023 · 63 citations
  57. LAB-Bench: Measuring Capabilities of Language Models for Biology ResearcharXiv:2407.10362Jon M. Laurent, Joseph D. Janizek, Michael Ruzo, et al.2024 · 62 citations
  58. Exploring the Impact of Instruction Data Scaling on Large Language Models: An Empirical Study on Real-World Use CasesarXiv:2303.14742Yunjie Ji, Yong Deng, Yan Gong, et al.2023 · 62 citations
  59. Rephrase and Respond: Let Large Language Models Ask Better Questions for ThemselvesarXiv:2311.04205Yihe Deng, Weitong Zhang, Zixiang Chen, et al.2023 · 62 citations
  60. SatLM: Satisfiability-Aided Language Models Using Declarative PromptingConference on Neural Information Processing Systems (NeurIPS)Xi Ye, Qiaochu Chen, Isil Dillig, et al.2023 · 62 citations
  61. What Language Model Architecture and Pretraining Objective Work Best for Zero-Shot Generalization?arXiv:2204.05832Thomas Wang, Adam Roberts, Daniel Hesslow, et al.2022 · 62 citations
  62. One-Shot Learning as Instruction Data Prospector for Large Language ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Yunshui Li, Binyuan Hui, Xiaobo Xia, et al.2024 · 61 citations
  63. Teaching Large Language Models to Reason with Reinforcement LearningarXiv:2403.04642Alex Havrilla, Yuqing Du, Sharath Chandra Raparthy, et al.2024 · 61 citations
  64. Large Language Models Are Not Strong Abstract ReasonersarXiv:2305.19555Gael Gendron, Qiming Bao, Michael Witbrock, et al.2023 · 61 citations
  65. Superfiltering: Weak-to-Strong Data Filtering for Fast Instruction-TuningarXiv:2402.00530Ming Li, Yong Zhang, Shwai He, et al.2024 · 60 citations
  66. Instruction Pre-Training: Language Models are Supervised Multitask LearnersConference on Empirical Methods in Natural Language Processing (EMNLP)Daixuan Cheng, Yuxian Gu, Shaohan Huang, et al.2024 · 59 citations
  67. Rethinking On-Policy Distillation of Large Language Models: Phenomenology, Mechanism, and RecipearXiv:2604.13016Yaxuan Li, Yuxin Zuo, Bingxiang He, et al.2026 · 58 citations
  68. BIG-Bench Extra HardAnnual Meeting of the Association for Computational Linguistics (ACL)Mehran Kazemi, Bahare Fatemi, Hritik Bansal, et al.2025 · 58 citations
  69. SoftCoT: Soft Chain-of-Thought for Efficient Reasoning with LLMsAnnual Meeting of the Association for Computational Linguistics (ACL)Yige Xu, Xu Guo, Zhiwei Zeng, et al.2025 · 58 citations
  70. ChatQA: Surpassing GPT-4 on Conversational QA and RAGConference on Neural Information Processing Systems (NeurIPS)Zihan Liu, Wei Ping, Rajarshi Roy, et al.2024 · 58 citations
  71. Beyond Chinchilla-Optimal: Accounting for Inference in Language Model Scaling LawsarXiv:2401.00448Nikhil Sardana, Jacob Portes, Sasha Doubov, et al.2024 · 57 citations
  72. SuperCLUE: A Comprehensive Chinese Large Language Model BenchmarkarXiv:2307.15020Liang Xu, Anqi Li, Lei Zhu, et al.2023 · 57 citations
  73. Teaching Arithmetic to Small TransformersarXiv:2307.03381Nayoung Lee, Kartik Sreenivasan, Jason D. Lee, et al.2023 · 57 citations
  74. The Art of Saying No: Contextual Noncompliance in Language ModelsConference on Neural Information Processing Systems (NeurIPS)Faeze Brahman, Sachin Kumar, Vidhisha Balachandran, et al.2024 · 56 citations
  75. A Chain-of-Thought Is as Strong as Its Weakest Link: A Benchmark for Verifiers of Reasoning ChainsAnnual Meeting of the Association for Computational Linguistics (ACL)Alon Jacovi, Yonatan Bitton, Bernd Bohnet, et al.2024 · 54 citations
  76. SEACrowd: A Multilingual Multimodal Data Hub and Benchmark Suite for Southeast Asian LanguagesConference on Empirical Methods in Natural Language Processing (EMNLP)Holy Lovenia, Rahmad Mahendra, Salsabil Maulana Akbar, et al.2024 · 54 citations
  77. CMB: A Comprehensive Medical Benchmark in ChinesearXiv:2308.08833Xidong Wang, Guiming Hardy Chen, Dingjie Song, et al.2023 · 54 citations
  78. Unifying AI Tutor Evaluation: An Evaluation Taxonomy for Pedagogical Ability Assessment of LLM-Powered AI TutorsConference of the North American Chapter of the Association for Computational Linguistics (NAACL)Kaushal Kumar Maurya, Kv Aditya Srivatsa, Kseniia Petukhova, et al.2025 · 53 citations
  79. Can Long-Context Language Models Subsume Retrieval, RAG, SQL, and More?arXiv:2406.13121Jinhyuk Lee, Anthony Chen, Zhuyun Dai, et al.2024 · 53 citations
  80. RM-Bench: Benchmarking Reward Models of Language Models with Subtlety and StylearXiv:2410.16184Yantao Liu, Zijun Yao, Rui Min, et al.2024 · 53 citations
  81. Towards responsible development of generative AI for education: An evaluation-driven approachJurenka2024 · 53 citations
  82. Language acquisition: do children and language models follow similar learning stages?arXiv:2306.03586Linnea Evanson, Yair Lakretz, Jean-Rémi King2023 · 53 citations
  83. The Learnability of In-Context LearningConference on Neural Information Processing Systems (NeurIPS)Noam Wies, Yoav Levine, Amnon Shashua2023 · 53 citations
  84. Large Language Models are In-Context Semantic Reasoners rather than Symbolic ReasonersarXiv:2305.14825Xiaojuan Tang, Zilong Zheng, Jiaqi Li, et al.2023 · 52 citations
  85. Easy-to-Hard Generalization: Scalable Alignment Beyond Human SupervisionConference on Neural Information Processing Systems (NeurIPS)Zhiqing Sun, Longhui Yu, Yikang Shen, et al.2024 · 51 citations
  86. On the application of Large Language Models for language teaching and assessment technologyarXiv:2307.08393Andrew Caines, Luca Benedetto, Shiva Taslimipoor, et al.2023 · 51 citations
  87. How to Train Data-Efficient LLMsarXiv:2402.09668Noveen Sachdeva, Benjamin Coleman, Wang-Cheng Kang, et al.2024 · 50 citations
  88. Unpacking DPO and PPO: Disentangling Best Practices for Learning from Preference FeedbackConference on Neural Information Processing Systems (NeurIPS)Hamish Ivison, Yizhong Wang, Jiacheng Liu, et al.2024 · 50 citations
  89. TIGERScore: Towards Building Explainable Metric for All Text Generation TasksarXiv:2310.00752Dongfu Jiang, Yishan Li, Ge Zhang, et al.2023 · 50 citations
  90. 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
  91. Alice in Wonderland: Simple Tasks Showing Complete Reasoning Breakdown in State-Of-the-Art Large Language ModelsarXiv:2406.02061Marianna Nezhurina, Lucia Cipolina-Kun, Mehdi Cherti, et al.2024 · 48 citations
  92. GrammarGPT: Exploring Open-Source LLMs for Native Chinese Grammatical Error Correction with Supervised Fine-TuningLecture notes in computer scienceYaxin Fan; Feng Jiang; Peifeng Li;, Haizhou Li2023 · 48 citations
  93. Linguistic ambiguity analysis in ChatGPTarXiv:2302.06426Miguel Ortega-Martin, Oscar Garcia-Sierra, Alfonso Ardoiz, et al.2023 · 48 citations
  94. Improving the Validity of Automatically Generated Feedback via Reinforcement LearningLecture notes in computer scienceAlexander Scarlatos, Digory Smith, Simon Woodhead, et al.2024 · 47 citations
  95. Metaphor Understanding Challenge Dataset for LLMsAnnual Meeting of the Association for Computational Linguistics (ACL)Xiaoyu Tong, Rochelle Choenni, Martha Lewis, et al.2024 · 47 citations
  96. AutoPEFT: Automatic Configuration Search for Parameter-Efficient Fine-TuningTransactions of the Association for Computational Linguistics (TACL)Han Zhou, Xingchen Wan, Ivan Vulić, et al.2024 · 46 citations
  97. Language models scale reliably with over-training and on downstream tasksarXiv:2403.08540Samir Yitzhak Gadre, Georgios Smyrnis, Vaishaal Shankar, et al.2024 · 46 citations
  98. “According to . . . “: Prompting Language Models Improves Quoting from Pre-Training DataConference of the European Chapter of the Association for Computational Linguistics (EACL)Orion Weller, Marc Marone, Nathaniel Weir, et al.2024 · 46 citations
  99. GLoRe: When, Where, and How to Improve LLM Reasoning via Global and Local RefinementsarXiv:2402.10963Alex Havrilla, Sharath Raparthy, Christoforus Nalmpantis, et al.2024 · 45 citations
  100. Lost in Translation: Large Language Models in Non-English Content AnalysisarXiv:2306.07377Gabriel Nicholas, Aliya Bhatia2023 · 45 citations

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