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Who cites James L. McClelland

476 citations of their works from 448 papers of our library (of the 149,439 whose bibliographies we read) · 113 with a page here, the most cited first · page 1 of 2 · 1–100 · ← James L. McClelland

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  1. Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine TranslationConference on Empirical Methods in Natural Language Processing (EMNLP)Kyunghyun Cho, Bart van Merriënboer, Caglar Gulcehre, et al.2014 · 26,725 citations
  2. Chain-of-Thought Prompting Elicits Reasoning in Large Language ModelsarXiv:2201.11903Jason Wei, Xuezhi Wang, Dale Schuurmans, et al.2022 · 11,499 citations
  3. Deep Residual Learning for Image RecognitionarXiv:1512.03385Kaiming He, Xiangyu Zhang, Shaoqing Ren, et al.2015 · 9,425 citations
  4. Model-Agnostic Meta-Learning for Fast Adaptation of Deep NetworksarXiv:1703.03400Chelsea Finn, Pieter Abbeel, Sergey Levine2017 · 6,549 citations
  5. Emergent Abilities of Large Language ModelsarXiv:2206.07682Jason Wei, Yi Tay, Rishi Bommasani, et al.2022 · 2,269 citations
  6. The Rise and Potential of Large Language Model Based Agents: A SurveyScience China Information SciencesZhiheng Xi, Wenxiang Chen, Xin Guo, et al.2023 · 1,272 citations
  7. Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve ThemFindings of the Association for Computational LinguisticsMirac Suzgun, Nathan Scales, Nathanael Schärli, et al.2023 · 1,119 citations
  8. A Survey on In-context LearningarXiv:2301.00234Qingxiu Dong, Lei Li, Damai Dai, et al.2023 · 978 citations
  9. Explainability for Large Language Models: A SurveyACM Transactions on Intelligent Systems and TechnologyHaiyan Zhao, Hanjie Chen, Fan Yang, et al.2023 · 790 citations
  10. Towards Reasoning in Large Language Models: A SurveyFindings of the Association for Computational LinguisticsJie Huang, Kevin Chen-Chuan Chang2023 · 780 citations
  11. STaR: Bootstrapping Reasoning With ReasoningEric Zelikman, Yuhuai Wu, Jesse Mu, et al.2022 · 777 citations
  12. Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context LearningConference on Neural Information Processing Systems (NeurIPS)Haokun Liu, Derek Tam, Mohammed Muqeeth, et al.2022 · 754 citations
  13. Highway NetworksarXiv:1505.00387Rupesh Kumar Srivastava, Klaus Greff, Jürgen Schmidhuber2015 · 680 citations
  14. Similarity of Neural Network Representations RevisitedarXiv:1905.00414Simon Kornblith, Mohammad Norouzi, Honglak Lee, et al.2019 · 679 citations
  15. Deep Semantic Role Labeling: What Works and What's NextLuheng He, Kenton Lee, Mike Lewis, et al.2017 · 596 citations
  16. Language Models Don't Always Say What They Think: Unfaithful Explanations in Chain-of-Thought PromptingConference on Neural Information Processing Systems (NeurIPS)Miles Turpin, Julian Michael, Ethan Perez, et al.2023 · 586 citations
  17. Large Language Models Can Self-ImproveConference on Empirical Methods in Natural Language Processing (EMNLP)Jiaxin Huang, Shixiang Gu, Le Hou, et al.2023 · 575 citations
  18. The Ubuntu Dialogue Corpus: A Large Dataset for Research in Unstructured Multi-Turn Dialogue SystemsAnnual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL)Ryan Lowe, Nissan Pow, Iulian Serban, et al.2015 · 494 citations
  19. Do Prompt-Based Models Really Understand the Meaning of Their Prompts?Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)Albert Webson, Ellie Pavlick2022 · 407 citations
  20. Text Understanding with the Attention Sum Reader NetworkAnnual Meeting of the Association for Computational Linguistics (ACL)Rudolf Kadlec, Martin Schmid, Ondrej Bajgar, et al.2016 · 390 citations
  21. Augmented Language Models: a SurveyarXiv:2302.07842Grégoire Mialon, Roberto Dessì, Maria Lomeli, et al.2023 · 369 citations
  22. Are Emergent Abilities of Large Language Models a Mirage?Conference on Neural Information Processing Systems (NeurIPS)Rylan Schaeffer, Brando Miranda, Sanmi Koyejo2023 · 342 citations
  23. Can language models learn from explanations in context?Findings of the Association for Computational LinguisticsAndrew Lampinen, Ishita Dasgupta, Stephanie Chan, et al.2022 · 295 citations
  24. Text Classification via Large Language ModelsFindings of the Association for Computational LinguisticsXiaofei Sun, Xiaoya Li, Jiwei Li, et al.2023 · 292 citations
  25. Larger language models do in-context learning differentlyarXiv:2303.03846Jerry Wei, Jason Wei, Yi Tay, et al.2023 · 285 citations
  26. Trusting Your Evidence: Hallucinate Less with Context-aware DecodingConference of the North American Chapter of the Association for Computational Linguistics (NAACL)Weijia Shi, Xiaochuang Han, Mike Lewis, et al.2024 · 249 citations
  27. Transformers learn in-context by gradient descentarXiv:2212.07677Johannes von Oswald, Eyvind Niklasson, Ettore Randazzo, et al.2022 · 247 citations
  28. Language Models Are Greedy Reasoners: A Systematic Formal Analysis of Chain-of-ThoughtarXiv:2210.01240Abulhair Saparov, He He2022 · 244 citations
  29. What learning algorithm is in-context learning? Investigations with linear modelsarXiv:2211.15661Ekin Akyürek, Dale Schuurmans, Jacob Andreas, et al.2022 · 236 citations
  30. Understanding the Difficulty of Training TransformersConference on Empirical Methods in Natural Language Processing (EMNLP)Liyuan Liu, Xiaodong Liu, Jianfeng Gao, et al.2020 · 231 citations
  31. DataComp: In search of the next generation of multimodal datasetsConference on Neural Information Processing Systems (NeurIPS)Samir Yitzhak Gadre, Gabriel Ilharco, Alex Fang, et al.2023 · 224 citations
  32. FOLIO: Natural Language Reasoning with First-Order LogicarXiv:2209.00840Simeng Han, Hailey Schoelkopf, Yilun Zhao, et al.2022 · 219 citations
  33. Maieutic Prompting: Logically Consistent Reasoning with Recursive ExplanationsConference on Empirical Methods in Natural Language Processing (EMNLP)Jaehun Jung, Lianhui Qin, Sean Welleck, et al.2022 · 219 citations
  34. Same Task, More Tokens: the Impact of Input Length on the Reasoning Performance of Large Language ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Mosh Levy, Alon Jacoby, Yoav Goldberg2024 · 217 citations
  35. Making Language Models Better Reasoners with Step-Aware VerifierAnnual Meeting of the Association for Computational Linguistics (ACL)Yifei Li, Zeqi Lin, Shizhuo Zhang, et al.2023 · 217 citations
  36. Sources of Hallucination by Large Language Models on Inference TasksFindings of the Association for Computational LinguisticsNick McKenna, Tianyi Li, Liang Cheng, et al.2023 · 212 citations
  37. PersonaLLM: Investigating the Ability of Large Language Models to Express Personality TraitsFindings of the Association for Computational LinguisticsHang Jiang, Xiajie Zhang, Xubo Cao, et al.2023 · 211 citations
  38. What Can Transformers Learn In-Context? A Case Study of Simple Function ClassesConference on Neural Information Processing Systems (NeurIPS)Shivam Garg, Dimitris Tsipras, Percy Liang, et al.2022 · 209 citations
  39. Fine-Tuning or Retrieval? Comparing Knowledge Injection in LLMsConference on Empirical Methods in Natural Language Processing (EMNLP)Oded Ovadia, Menachem Brief, Moshik Mishaeli, et al.2024 · 206 citations
  40. The Unreliability of Explanations in Few-shot Prompting for Textual ReasoningConference on Neural Information Processing Systems (NeurIPS)Xi Ye, Greg Durrett2022 · 196 citations
  41. Deja Vu: Contextual Sparsity for Efficient LLMs at Inference TimeInternational Conference on Machine Learning (ICML)Zichang Liu, Jue Wang, Tri Dao, et al.2023 · 185 citations
  42. Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language ModelsScience China Information SciencesQiguang Chen, Libo Qin, Jinhao Liu, et al.2025 · 176 citations
  43. Evaluating and Inducing Personality in Pre-trained Language ModelsConference on Neural Information Processing Systems (NeurIPS)Guangyuan Jiang, Manjie Xu, Song-Chun Zhu, et al.2022 · 168 citations
  44. Impact of Pretraining Term Frequencies on Few-Shot Numerical ReasoningFindings of the Association for Computational LinguisticsYasaman Razeghi, Robert L Logan IV, Matt Gardner, et al.2022 · 165 citations
  45. Language models show human-like content effects on reasoning tasksarXiv:2207.07051Ishita Dasgupta, Andrew K. Lampinen, Stephanie C. Y. Chan, et al.2022 · 162 citations
  46. Reasoning with Language Model Prompting: A SurveyarXiv:2212.09597Shuofei Qiao, Yixin Ou, Ningyu Zhang, et al.2022 · 161 citations
  47. In-Context Learning Creates Task VectorsFindings of the Association for Computational LinguisticsRoee Hendel, Mor Geva, Amir Globerson2023 · 159 citations
  48. Draft & Verify: Lossless Large Language Model Acceleration via Self-Speculative DecodingAnnual Meeting of the Association for Computational Linguistics (ACL)Jun Zhang, Jue Wang, Huan Li, et al.2024 · 148 citations
  49. Language Model Behavior: A Comprehensive SurveyComputational LinguisticsTyler A. Chang, Benjamin K. Bergen2024 · 148 citations
  50. Do LLMs Exhibit Human-like Response Biases? A Case Study in Survey DesignTransactions of the Association for Computational Linguistics (TACL)Lindia Tjuatja, Valerie Chen, Tongshuang Wu, et al.2024 · 140 citations
  51. MathDial: A Dialogue Tutoring Dataset with Rich Pedagogical Properties Grounded in Math Reasoning ProblemsFindings of the Association for Computational LinguisticsJakub Macina, Nico Daheim, Sankalan Chowdhury, et al.2023 · 131 citations
  52. Deductive Verification of Chain-of-Thought ReasoningConference on Neural Information Processing Systems (NeurIPS)Zhan Ling, Yunhao Fang, Xuanlin Li, et al.2023 · 130 citations
  53. Natural Language Reasoning, A SurveyACM Computing SurveysFei Yu, Hongbo Zhang, Prayag Tiwari, et al.2023 · 130 citations
  54. Automatic Prompt Augmentation and Selection with Chain-of-Thought from Labeled DataFindings of the Association for Computational LinguisticsKashun Shum, Shizhe Diao, Tong Zhang2023 · 128 citations
  55. Navigate through Enigmatic Labyrinth A Survey of Chain of Thought Reasoning: Advances, Frontiers and FuturearXiv:2309.15402Zheng Chu, Jingchang Chen, Qianglong Chen, et al.2023 · 125 citations
  56. Quiet-STaR: Language models can teach themselves to think before speaking2024 · 122 citations
  57. Faithful Reasoning Using Large Language ModelsarXiv:2208.14271Antonia Creswell, Murray Shanahan2022 · 122 citations
  58. Explanations from Large Language Models Make Small Reasoners BetterarXiv:2210.06726Shiyang Li, Jianshu Chen, Yelong Shen, et al.2022 · 120 citations
  59. Large Language Models Are Latent Variable Models: Explaining and Finding Good Demonstrations for In-Context LearningConference on Neural Information Processing Systems (NeurIPS)Xinyi Wang, Wanrong Zhu, Michael Saxon, et al.2023 · 116 citations
  60. Reframing Human-AI Collaboration for Generating Free-Text ExplanationsarXiv:2112.08674Sarah Wiegreffe, Jack Hessel, Swabha Swayamdipta, et al.2021 · 111 citations
  61. What In-Context Learning “Learns” In-Context: Disentangling Task Recognition and Task LearningFindings of the Association for Computational LinguisticsJane Pan, Tianyu Gao, Howard Chen, et al.2023 · 102 citations
  62. Rationale-Augmented Ensembles in Language ModelsarXiv:2207.00747Xuezhi Wang, Jason Wei, Dale Schuurmans, et al.2022 · 101 citations
  63. Large Language Models as General Pattern MachinesarXiv:2307.04721Suvir Mirchandani, Fei Xia, Pete Florence, et al.2023 · 88 citations
  64. Foundational Challenges in Assuring Alignment and Safety of Large Language ModelsarXiv:2404.09932Usman Anwar, Abulhair Saparov, Javier Rando, et al.2024 · 87 citations
  65. Rethinking Interpretability in the Era of Large Language ModelsarXiv:2402.01761Chandan Singh, Jeevana Priya Inala, Michel Galley, et al.2024 · 87 citations
  66. Reward Design with Language ModelsarXiv:2303.00001Minae Kwon, Sang Michael Xie, Kalesha Bullard, et al.2023 · 73 citations
  67. C3oT: Generating Shorter Chain-of-Thought without Compromising EffectivenessProceedings of the AAAI Conference on Artificial IntelligenceYu Kang, Xianghui Sun, Liangyu Chen, et al.2024 · 66 citations
  68. 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
  69. Can Language Models Handle Recursively Nested Grammatical Structures? A Case Study on Comparing Models and HumansComputational LinguisticsAndrew Lampinen2024 · 63 citations
  70. Parsel: Algorithmic Reasoning with Language Models by Composing DecompositionsConference on Neural Information Processing Systems (NeurIPS)Eric Zelikman, Qian Huang, Gabriel Poesia, et al.2023 · 56 citations
  71. Beyond Accuracy: Evaluating the Reasoning Behavior of Large Language Models — A SurveyarXiv:2404.01869Philipp Mondorf, Barbara Plank2024 · 53 citations
  72. The Learnability of In-Context LearningConference on Neural Information Processing Systems (NeurIPS)Noam Wies, Yoav Levine, Amnon Shashua2023 · 53 citations
  73. Understanding Catastrophic Forgetting in Language Models via Implicit InferencearXiv:2309.10105Suhas Kotha, Jacob Mitchell Springer, Aditi Raghunathan2023 · 45 citations
  74. Generating and Exploiting Large-scale Pseudo Training Data for Zero Pronoun ResolutionAnnual Meeting of the Association for Computational Linguistics (ACL)Ting Liu, Yiming Cui, Qingyu Yin, et al.2017 · 37 citations
  75. The Life Cycle of Knowledge in Big Language Models: A SurveyarXiv:2303.07616 · Machine Intelligence Research. vol. 21, no. 2, pp. 217-238, 2024Boxi Cao, Hongyu Lin, Xianpei Han, et al.2023 · 31 citations
  76. Analyzing Chain-of-Thought Prompting in Large Language Models via Gradient-based Feature AttributionsarXiv:2307.13339Skyler Wu, Eric Meng Shen, Charumathi Badrinath, et al.2023 · 30 citations
  77. The Quantization Model of Neural ScalingarXiv:2303.13506Eric J. Michaud, Ziming Liu, Uzay Girit, et al.2023 · 30 citations
  78. Towards LogiGLUE: A Brief Survey and A Benchmark for Analyzing Logical Reasoning Capabilities of Language ModelsarXiv:2310.00836Man Luo, Shrinidhi Kumbhar, Ming shen, et al.2023 · 27 citations
  79. SemEval-2026 Task 11: Disentangling Content and Formal Reasoning in Large Language ModelsInternational Workshop on Semantic Evaluation (SemEval)Marco Valentino, Leonardo Ranaldi, Giulia Pucci, et al.2026 · 22 citations
  80. Humans Learn Language from Situated Communicative Interactions. What about Machines?Computational LinguisticsKatrien Beuls, Paul Van Eecke2024 · 21 citations
  81. Understanding Emergent In-Context Learning from a Kernel Regression PerspectivearXiv:2305.12766Chi Han, Ziqi Wang, Han Zhao, et al.2023 · 21 citations
  82. Ecologically Valid Explanations for Label Variation in NLIFindings of the Association for Computational LinguisticsNan-Jiang Jiang, Chenhao Tan, Marie-Catherine de Marneffe2023 · 19 citations
  83. “Seeing the Big through the Small”: Can LLMs Approximate Human Judgment Distributions on NLI from a Few Explanations?Findings of the Association for Computational LinguisticsBeiduo Chen, Xinpeng Wang, Siyao Peng, et al.2024 · 18 citations
  84. Evaluating Large Language Models with NeuBAROCO: Syllogistic Reasoning Ability and Human-like BiasesarXiv:2306.12567Risako Ando, Takanobu Morishita, Hirohiko Abe, et al.2023 · 17 citations
  85. Between Circuits and Chomsky: Pre-pretraining on Formal Languages Imparts Linguistic BiasesAnnual Meeting of the Association for Computational Linguistics (ACL)Michael Y. Hu, Jackson Petty, Chuan Shi, et al.2025 · 9 citations
  86. Pixels Versus Priors: Controlling Knowledge Priors in Vision-Language Models through Visual CounterfactsConference on Empirical Methods in Natural Language Processing (EMNLP)Michal Golovanevsky, William Rudman, Michael A. Lepori, et al.2025 · 8 citations
  87. Unveiling Language Competence Neurons: A Psycholinguistic Approach to Model InterpretabilityInternational Conference on Computational Linguistics (COLING)Xufeng Duan, Xinyu Zhou, Bei Xiao, et al.2025 · 8 citations
  88. Entailed Between the Lines: Incorporating Implication into NLIAnnual Meeting of the Association for Computational Linguistics (ACL)Shreya Havaldar, Hamidreza Alvari, John Palowitch, et al.2025 · 6 citations
  89. Rethinking Prompt Optimizers: From Prompt Merits to OptimizationConference of the European Chapter of the Association for Computational Linguistics (EACL)Zixiao Zhu, Hanzhang Zhou, Zijian Feng, et al.2026 · 3 citations
  90. Semantic Change Characterization with LLMs using RhetoricsProceedings for the International Workshop on Computational Approaches to Language ChangeJáder Martins Camboim de Sá, Jooyoung Lee, Marcos Da Silveira, et al.2026 · 3 citations
  91. No Context Needed: Contextual Quandary In Idiomatic Reasoning With Pre-Trained Language ModelsConference of the North American Chapter of the Association for Computational Linguistics (NAACL)Kellen Tan Cheng, Suma Bhat2024 · 3 citations
  92. Once Correct, Still Wrong: Counterfactual Hallucination in Multilingual Vision-Language ModelsFindings of the Association for Computational LinguisticsBasel Mousi, Fahim Dalvi, Shammur Absar Chowdhury, et al.2026 · 2 citations
  93. AICOE-Tredence at SemEval-2026 Task 11: Mitigating Content Bias in Syllogisms via Symbolic Logic-Language DecouplingInternational Workshop on Semantic Evaluation (SemEval)Rakshith R, Ankush Chopra2026 · 1 citation
  94. BIS Reasoning 1.0: The First Large-Scale Japanese Benchmark for Belief-Inconsistent Syllogistic ReasoningLanguage Resources and Evaluation Conference (LREC)Ha Thanh Nguyen, Hideyuki Tachibana, Chaoran Liu, et al.2026 · 1 citation
  95. Chain-of-Thought as a Lens: Evaluating Structured Reasoning Alignment between Human Preferences and Large Language ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Boxuan Wang, Zhuoyun Li, Xinmiao Huang, et al.2026 · 1 citation
  96. Ellat at SemEval-2026 Task 11: Comparing Encoder and Decoder Models for Syllogistic ReasoningInternational Workshop on Semantic Evaluation (SemEval)Farzaneh Bayan Memar, Hanneke Huls, Matthijs Ten Hove2026 · 1 citation
  97. GIL-Zaragoza at SemEval 2026 Task 11: Comparing Classification, Autoformalization, and Ontologies for Formal Reasoning CapabilitiesInternational Workshop on Semantic Evaluation (SemEval)Francisco Lopez-Ponce, Lucia Pitarch, Iván Saavedra Martínez, et al.2026 · 1 citation
  98. HABIB_TAZ at SemEval-2026 Task 11: Disentangling Formal Logic from Content via Synthetic Training and Multi-Objective OptimizationInternational Workshop on Semantic Evaluation (SemEval)Abdullah Shaikh, Zain Naqi, Taha Zahid, et al.2026 · 1 citation
  99. Hidetsune at SemEval-2026 Task 11: Adapting Pretrained Reasoning Models with Deep Supervision and Inference Refinement for Content-Independent Validity ClassificationInternational Workshop on Semantic Evaluation (SemEval)Hidetsune Takahashi2026 · 1 citation
  100. ITLC at SemEval-2026 Task 11: Normalization and Deterministic Parsing for Formal Reasoning in LLMsInternational Workshop on Semantic Evaluation (SemEval)Wicaksono Leksono Muhamad, Joanito Agili Lopo, Tack Hwa Wong, et al.2026 · 1 citation

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