---
title: "Who cites James L. McClelland — 448 papers of our library"
description: "448 papers of the Elamur Research library cite the works of James L. McClelland, found in their bibliographies · page 1 of 2 · 1–100; the most…"
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---
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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](https://md.research.elamur.ai/free/en/authors/james-l-mcclelland/)

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1. **[Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation](https://md.research.elamur.ai/free/en/papers/cho-2014-learning-phrase-representations-rnn-encoder/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Kyunghyun Cho, Bart van Merriënboer, Caglar Gulcehre, et al.*2014 · 26,725 citations*
2. [**Chain-of-Thought Prompting Elicits Reasoning in Large Language Models arXiv:2201.11903**Jason Wei, Xuezhi Wang, Dale Schuurmans, et al.*2022 · 11,499 citations*](https://md.research.elamur.ai/free/en/papers/wei-2022-chain-thought-prompting-elicits-reasoning/)
3. [**Deep Residual Learning for Image Recognition arXiv:1512.03385**Kaiming He, Xiangyu Zhang, Shaoqing Ren, et al.*2015 · 9,425 citations*](https://md.research.elamur.ai/free/en/papers/he-2015-deep-residual-learning-image-recognition/)
4. [**Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks arXiv:1703.03400**Chelsea Finn, Pieter Abbeel, Sergey Levine*2017 · 6,549 citations*](https://md.research.elamur.ai/free/en/papers/finn-2017-model-agnostic-meta-learning-fast/)
5. [**Emergent Abilities of Large Language Models arXiv:2206.07682**Jason Wei, Yi Tay, Rishi Bommasani, et al.*2022 · 2,269 citations*](https://md.research.elamur.ai/free/en/papers/wei-2022-emergent-abilities-large-language-models/)
6. **[The Rise and Potential of Large Language Model Based Agents: A Survey](https://md.research.elamur.ai/free/en/papers/xi-2023-rise-potential-large-language/) [Science China Information Sciences](https://md.research.elamur.ai/free/en/venues/science-china-information-sciences/)**Zhiheng Xi, Wenxiang Chen, Xin Guo, et al.*2023 · 1,272 citations*
7. **[Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them](https://md.research.elamur.ai/free/en/papers/suzgun-2023-challenging-big-bench-tasks-whether/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Mirac Suzgun, Nathan Scales, Nathanael Schärli, et al.*2023 · 1,119 citations*
8. [**A Survey on In-context Learning arXiv:2301.00234**Qingxiu Dong, Lei Li, Damai Dai, et al.*2023 · 978 citations*](https://md.research.elamur.ai/free/en/papers/dong-2023-survey-context-learning/)
9. **[Explainability for Large Language Models: A Survey](https://md.research.elamur.ai/free/en/papers/zhao-2023-explainability-large-language-models-survey/) [ACM Transactions on Intelligent Systems and Technology](https://md.research.elamur.ai/free/en/venues/acm-transactions-on-intelligent-systems-and-technology/)**Haiyan Zhao, Hanjie Chen, Fan Yang, et al.*2023 · 790 citations*
10. **[Towards Reasoning in Large Language Models: A Survey](https://md.research.elamur.ai/free/en/papers/huang-2023-reasoning-large-language-models-survey/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Jie Huang, Kevin Chen-Chuan Chang*2023 · 780 citations*
11. [**STaR: Bootstrapping Reasoning With Reasoning**Eric Zelikman, Yuhuai Wu, Jesse Mu, et al.*2022 · 777 citations*](https://md.research.elamur.ai/free/en/papers/zelikman-2022-star-bootstrapping-reasoning-reasoning/)
12. **[Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning](https://md.research.elamur.ai/free/en/papers/liu-2022-few-shot-parameter-efficient-fine/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Haokun Liu, Derek Tam, Mohammed Muqeeth, et al.*2022 · 754 citations*
13. [**Highway Networks arXiv:1505.00387**Rupesh Kumar Srivastava, Klaus Greff, Jürgen Schmidhuber*2015 · 680 citations*](https://md.research.elamur.ai/free/en/papers/srivastava-2015-highway-networks/)
14. [**Similarity of Neural Network Representations Revisited arXiv:1905.00414**Simon Kornblith, Mohammad Norouzi, Honglak Lee, et al.*2019 · 679 citations*](https://md.research.elamur.ai/free/en/papers/kornblith-2019-similarity-neural-network-representations-revisited/)
15. [**Deep Semantic Role Labeling: What Works and What's Next**Luheng He, Kenton Lee, Mike Lewis, et al.*2017 · 596 citations*](https://md.research.elamur.ai/free/en/papers/he-2017-deep-semantic-role-labeling-what/)
16. **[Language Models Don't Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Prompting](https://md.research.elamur.ai/free/en/papers/turpin-2023-language-models-don-t-always/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Miles Turpin, Julian Michael, Ethan Perez, et al.*2023 · 586 citations*
17. **[Large Language Models Can Self-Improve](https://md.research.elamur.ai/free/en/papers/huang-2023-large-language-models-can-self/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/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 Systems](https://md.research.elamur.ai/free/en/papers/lowe-2015-ubuntu-dialogue-corpus-large-dataset/) [Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL)](https://md.research.elamur.ai/free/en/venues/sigdial/)**Ryan Lowe, Nissan Pow, Iulian Serban, et al.*2015 · 494 citations*
19. **[Do Prompt-Based Models Really Understand the Meaning of Their Prompts?](https://md.research.elamur.ai/free/en/papers/webson-2022-do-prompt-based-models-really/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Albert Webson, Ellie Pavlick*2022 · 407 citations*
20. **[Text Understanding with the Attention Sum Reader Network](https://md.research.elamur.ai/free/en/papers/kadlec-2016-text-understanding-attention-sum-reader/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Rudolf Kadlec, Martin Schmid, Ondrej Bajgar, et al.*2016 · 390 citations*
21. [**Augmented Language Models: a Survey arXiv:2302.07842**Grégoire Mialon, Roberto Dessì, Maria Lomeli, et al.*2023 · 369 citations*](https://md.research.elamur.ai/free/en/papers/mialon-2023-augmented-language-models-survey/)
22. **[Are Emergent Abilities of Large Language Models a Mirage?](https://md.research.elamur.ai/free/en/papers/schaeffer-2023-emergent-abilities-large-language-models/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Rylan Schaeffer, Brando Miranda, Sanmi Koyejo*2023 · 342 citations*
23. **[Can language models learn from explanations in context?](https://md.research.elamur.ai/free/en/papers/lampinen-2022-can-language-models-learn-explanations/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Andrew Lampinen, Ishita Dasgupta, Stephanie Chan, et al.*2022 · 295 citations*
24. **[Text Classification via Large Language Models](https://md.research.elamur.ai/free/en/papers/sun-2023-text-classification-large-language-models/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Xiaofei Sun, Xiaoya Li, Jiwei Li, et al.*2023 · 292 citations*
25. [**Larger language models do in-context learning differently arXiv:2303.03846**Jerry Wei, Jason Wei, Yi Tay, et al.*2023 · 285 citations*](https://md.research.elamur.ai/free/en/papers/wei-2023-larger-language-models-context-learning/)
26. **[Trusting Your Evidence: Hallucinate Less with Context-aware Decoding](https://md.research.elamur.ai/free/en/papers/shi-2024-trusting-your-evidence-hallucinate-less/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Weijia Shi, Xiaochuang Han, Mike Lewis, et al.*2024 · 249 citations*
27. [**Transformers learn in-context by gradient descent arXiv:2212.07677**Johannes von Oswald, Eyvind Niklasson, Ettore Randazzo, et al.*2022 · 247 citations*](https://md.research.elamur.ai/free/en/papers/oswald-2022-transformers-learn-context-gradient-descent/)
28. [**Language Models Are Greedy Reasoners: A Systematic Formal Analysis of Chain-of-Thought arXiv:2210.01240**Abulhair Saparov, He He*2022 · 244 citations*](https://md.research.elamur.ai/free/en/papers/saparov-2022-language-models-greedy-reasoners-systematic/)
29. [**What learning algorithm is in-context learning? Investigations with linear models arXiv:2211.15661**Ekin Akyürek, Dale Schuurmans, Jacob Andreas, et al.*2022 · 236 citations*](https://md.research.elamur.ai/free/en/papers/akyurek-2022-learning-algorithm-context-learning-investigations/)
30. **[Understanding the Difficulty of Training Transformers](https://md.research.elamur.ai/free/en/papers/liu-2020-understanding-difficulty-training-transformers/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Liyuan Liu, Xiaodong Liu, Jianfeng Gao, et al.*2020 · 231 citations*
31. **[DataComp: In search of the next generation of multimodal datasets](https://md.research.elamur.ai/free/en/papers/gadre-2023-datacomp-search-next-generation-multimodal/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Samir Yitzhak Gadre, Gabriel Ilharco, Alex Fang, et al.*2023 · 224 citations*
32. [**FOLIO: Natural Language Reasoning with First-Order Logic arXiv:2209.00840**Simeng Han, Hailey Schoelkopf, Yilun Zhao, et al.*2022 · 219 citations*](https://md.research.elamur.ai/free/en/papers/han-2022-folio-natural-language-reasoning-first/)
33. **[Maieutic Prompting: Logically Consistent Reasoning with Recursive Explanations](https://md.research.elamur.ai/free/en/papers/jung-2022-maieutic-prompting-logically-consistent-reasoning/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/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 Models](https://md.research.elamur.ai/free/en/papers/levy-2024-same-task-more-tokens-impact/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Mosh Levy, Alon Jacoby, Yoav Goldberg*2024 · 217 citations*
35. **[Making Language Models Better Reasoners with Step-Aware Verifier](https://md.research.elamur.ai/free/en/papers/li-2023-making-language-models-better-reasoners/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yifei Li, Zeqi Lin, Shizhuo Zhang, et al.*2023 · 217 citations*
36. **[Sources of Hallucination by Large Language Models on Inference Tasks](https://md.research.elamur.ai/free/en/papers/mckenna-2023-sources-hallucination-large-language-models/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Nick McKenna, Tianyi Li, Liang Cheng, et al.*2023 · 212 citations*
37. **[PersonaLLM: Investigating the Ability of Large Language Models to Express Personality Traits](https://md.research.elamur.ai/free/en/papers/jiang-2023-personallm-investigating-ability-large-language/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Hang Jiang, Xiajie Zhang, Xubo Cao, et al.*2023 · 211 citations*
38. **[What Can Transformers Learn In-Context? A Case Study of Simple Function Classes](https://md.research.elamur.ai/free/en/papers/garg-2022-transformers-learn-context-case-study/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Shivam Garg, Dimitris Tsipras, Percy Liang, et al.*2022 · 209 citations*
39. **[Fine-Tuning or Retrieval? Comparing Knowledge Injection in LLMs](https://md.research.elamur.ai/free/en/papers/ovadia-2024-fine-tuning-or-retrieval-comparing/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Oded Ovadia, Menachem Brief, Moshik Mishaeli, et al.*2024 · 206 citations*
40. **[The Unreliability of Explanations in Few-shot Prompting for Textual Reasoning](https://md.research.elamur.ai/free/en/papers/ye-2022-unreliability-explanations-few-shot-prompting/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Xi Ye, Greg Durrett*2022 · 196 citations*
41. **[Deja Vu: Contextual Sparsity for Efficient LLMs at Inference Time](https://md.research.elamur.ai/free/en/papers/liu-2023-deja-vu-contextual-sparsity-efficient/) [International Conference on Machine Learning (ICML)](https://md.research.elamur.ai/free/en/venues/international-conference-on-machine-learning/)**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 Models](https://md.research.elamur.ai/free/en/papers/chen-2025-towards-reasoning-era-survey-long/) [Science China Information Sciences](https://md.research.elamur.ai/free/en/venues/science-china-information-sciences/)**Qiguang Chen, Libo Qin, Jinhao Liu, et al.*2025 · 176 citations*
43. **[Evaluating and Inducing Personality in Pre-trained Language Models](https://md.research.elamur.ai/free/en/papers/jiang-2022-evaluating-inducing-personality-pre-trained/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Guangyuan Jiang, Manjie Xu, Song-Chun Zhu, et al.*2022 · 168 citations*
44. **[Impact of Pretraining Term Frequencies on Few-Shot Numerical Reasoning](https://md.research.elamur.ai/free/en/papers/razeghi-2022-impact-pretraining-term-frequencies-few/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yasaman Razeghi, Robert L Logan IV, Matt Gardner, et al.*2022 · 165 citations*
45. [**Language models show human-like content effects on reasoning tasks arXiv:2207.07051**Ishita Dasgupta, Andrew K. Lampinen, Stephanie C. Y. Chan, et al.*2022 · 162 citations*](https://md.research.elamur.ai/free/en/papers/dasgupta-2022-language-models-show-human-like/)
46. [**Reasoning with Language Model Prompting: A Survey arXiv:2212.09597**Shuofei Qiao, Yixin Ou, Ningyu Zhang, et al.*2022 · 161 citations*](https://md.research.elamur.ai/free/en/papers/qiao-2022-reasoning-language-model-prompting-survey/)
47. **[In-Context Learning Creates Task Vectors](https://md.research.elamur.ai/free/en/papers/hendel-2023-context-learning-creates-task-vectors/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Roee Hendel, Mor Geva, Amir Globerson*2023 · 159 citations*
48. **[Draft & Verify: Lossless Large Language Model Acceleration via Self-Speculative Decoding](https://md.research.elamur.ai/free/en/papers/zhang-2024-draft-verify-lossless-large-language/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jun Zhang, Jue Wang, Huan Li, et al.*2024 · 148 citations*
49. **[Language Model Behavior: A Comprehensive Survey](https://md.research.elamur.ai/free/en/papers/chang-2024-language-model-behavior-comprehensive-survey/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Tyler A. Chang, Benjamin K. Bergen*2024 · 148 citations*
50. **[Do LLMs Exhibit Human-like Response Biases? A Case Study in Survey Design](https://md.research.elamur.ai/free/en/papers/tjuatja-2024-do-llms-exhibit-human-like/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/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 Problems](https://md.research.elamur.ai/free/en/papers/macina-2023-mathdial-dialogue-tutoring-dataset-rich/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Jakub Macina, Nico Daheim, Sankalan Chowdhury, et al.*2023 · 131 citations*
52. **[Deductive Verification of Chain-of-Thought Reasoning](https://md.research.elamur.ai/free/en/papers/ling-2023-deductive-verification-chain-thought-reasoning/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Zhan Ling, Yunhao Fang, Xuanlin Li, et al.*2023 · 130 citations*
53. **[Natural Language Reasoning, A Survey](https://md.research.elamur.ai/free/en/papers/yu-2023-natural-language-reasoning-survey/) [ACM Computing Surveys](https://md.research.elamur.ai/free/en/venues/acm-computing-surveys/)**Fei Yu, Hongbo Zhang, Prayag Tiwari, et al.*2023 · 130 citations*
54. **[Automatic Prompt Augmentation and Selection with Chain-of-Thought from Labeled Data](https://md.research.elamur.ai/free/en/papers/shum-2023-automatic-prompt-augmentation-selection-chain/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Kashun Shum, Shizhe Diao, Tong Zhang*2023 · 128 citations*
55. [**Navigate through Enigmatic Labyrinth A Survey of Chain of Thought Reasoning: Advances, Frontiers and Future arXiv:2309.15402**Zheng Chu, Jingchang Chen, Qianglong Chen, et al.*2023 · 125 citations*](https://md.research.elamur.ai/free/en/papers/chu-2023-navigate-through-enigmatic-labyrinth-survey/)
56. [**Quiet-STaR: Language models can teach themselves to think before speaking***2024 · 122 citations*](https://md.research.elamur.ai/free/en/papers/al-2024-quiet-star-language-models-teach/)
57. [**Faithful Reasoning Using Large Language Models arXiv:2208.14271**Antonia Creswell, Murray Shanahan*2022 · 122 citations*](https://md.research.elamur.ai/free/en/papers/creswell-2022-faithful-reasoning-using-large-language/)
58. [**Explanations from Large Language Models Make Small Reasoners Better arXiv:2210.06726**Shiyang Li, Jianshu Chen, Yelong Shen, et al.*2022 · 120 citations*](https://md.research.elamur.ai/free/en/papers/li-2022-explanations-large-language-models-make/)
59. **[Large Language Models Are Latent Variable Models: Explaining and Finding Good Demonstrations for In-Context Learning](https://md.research.elamur.ai/free/en/papers/wang-2023-large-language-models-latent-variable/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Xinyi Wang, Wanrong Zhu, Michael Saxon, et al.*2023 · 116 citations*
60. [**Reframing Human-AI Collaboration for Generating Free-Text Explanations arXiv:2112.08674**Sarah Wiegreffe, Jack Hessel, Swabha Swayamdipta, et al.*2021 · 111 citations*](https://md.research.elamur.ai/free/en/papers/wiegreffe-2021-reframing-human-ai-collaboration-generating/)
61. **[What In-Context Learning “Learns” In-Context: Disentangling Task Recognition and Task Learning](https://md.research.elamur.ai/free/en/papers/pan-2023-what-context-learning-learns-context/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Jane Pan, Tianyu Gao, Howard Chen, et al.*2023 · 102 citations*
62. [**Rationale-Augmented Ensembles in Language Models arXiv:2207.00747**Xuezhi Wang, Jason Wei, Dale Schuurmans, et al.*2022 · 101 citations*](https://md.research.elamur.ai/free/en/papers/wang-2022-rationale-augmented-ensembles-language-models/)
63. [**Large Language Models as General Pattern Machines arXiv:2307.04721**Suvir Mirchandani, Fei Xia, Pete Florence, et al.*2023 · 88 citations*](https://md.research.elamur.ai/free/en/papers/mirchandani-2023-large-language-models-general-pattern/)
64. [**Foundational Challenges in Assuring Alignment and Safety of Large Language Models arXiv:2404.09932**Usman Anwar, Abulhair Saparov, Javier Rando, et al.*2024 · 87 citations*](https://md.research.elamur.ai/free/en/papers/anwar-2024-foundational-challenges-assuring-alignment-safety/)
65. [**Rethinking Interpretability in the Era of Large Language Models arXiv:2402.01761**Chandan Singh, Jeevana Priya Inala, Michel Galley, et al.*2024 · 87 citations*](https://md.research.elamur.ai/free/en/papers/singh-2024-rethinking-interpretability-era-large-language/)
66. [**Reward Design with Language Models arXiv:2303.00001**Minae Kwon, Sang Michael Xie, Kalesha Bullard, et al.*2023 · 73 citations*](https://md.research.elamur.ai/free/en/papers/kwon-2023-reward-design-language-models/)
67. **[C3oT: Generating Shorter Chain-of-Thought without Compromising Effectiveness](https://md.research.elamur.ai/free/en/papers/kang-2024-c3ot-generating-shorter-chain-thought/) [Proceedings of the AAAI Conference on Artificial Intelligence](https://md.research.elamur.ai/free/en/venues/proceedings-of-the-aaai-conference-on-artificial-intelligence/)**Yu Kang, Xianghui Sun, Liangyu Chen, et al.*2024 · 66 citations*
68. **[A Peek into Token Bias: Large Language Models Are Not Yet Genuine Reasoners](https://md.research.elamur.ai/free/en/papers/jiang-2024-peek-into-token-bias-large/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/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 Humans](https://md.research.elamur.ai/free/en/papers/lampinen-2024-can-language-models-handle-recursively/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Andrew Lampinen*2024 · 63 citations*
70. **[Parsel: Algorithmic Reasoning with Language Models by Composing Decompositions](https://md.research.elamur.ai/free/en/papers/zelikman-2023-parsel-algorithmic-reasoning-language-models/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Eric Zelikman, Qian Huang, Gabriel Poesia, et al.*2023 · 56 citations*
71. [**Beyond Accuracy: Evaluating the Reasoning Behavior of Large Language Models — A Survey arXiv:2404.01869**Philipp Mondorf, Barbara Plank*2024 · 53 citations*](https://md.research.elamur.ai/free/en/papers/mondorf-2024-accuracy-evaluating-reasoning-behavior-large/)
72. **[The Learnability of In-Context Learning](https://md.research.elamur.ai/free/en/papers/wies-2023-learnability-context-learning/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Noam Wies, Yoav Levine, Amnon Shashua*2023 · 53 citations*
73. [**Understanding Catastrophic Forgetting in Language Models via Implicit Inference arXiv:2309.10105**Suhas Kotha, Jacob Mitchell Springer, Aditi Raghunathan*2023 · 45 citations*](https://md.research.elamur.ai/free/en/papers/kotha-2023-understanding-catastrophic-forgetting-language-models/)
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75. [**The Life Cycle of Knowledge in Big Language Models: A Survey arXiv:2303.07616 · Machine Intelligence Research. vol. 21, no. 2, pp. 217-238, 2024**Boxi Cao, Hongyu Lin, Xianpei Han, et al.*2023 · 31 citations*](https://md.research.elamur.ai/free/en/papers/cao-2023-life-cycle-knowledge-big-language/)
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78. [**Towards LogiGLUE: A Brief Survey and A Benchmark for Analyzing Logical Reasoning Capabilities of Language Models arXiv:2310.00836**Man Luo, Shrinidhi Kumbhar, Ming shen, et al.*2023 · 27 citations*](https://md.research.elamur.ai/free/en/papers/luo-2023-towards-logiglue-brief-survey-benchmark/)
79. **[SemEval-2026 Task 11: Disentangling Content and Formal Reasoning in Large Language Models](https://md.research.elamur.ai/free/en/papers/valentino-2026-semeval-2026-task-11-disentangling/) [International Workshop on Semantic Evaluation (SemEval)](https://md.research.elamur.ai/free/en/venues/semeval/)**Marco Valentino, Leonardo Ranaldi, Giulia Pucci, et al.*2026 · 22 citations*
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83. **[“Seeing the Big through the Small”: Can LLMs Approximate Human Judgment Distributions on NLI from a Few Explanations?](https://md.research.elamur.ai/free/en/papers/chen-2024-seeing-big-through-small-can/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Beiduo Chen, Xinpeng Wang, Siyao Peng, et al.*2024 · 18 citations*
84. [**Evaluating Large Language Models with NeuBAROCO: Syllogistic Reasoning Ability and Human-like Biases arXiv:2306.12567**Risako Ando, Takanobu Morishita, Hirohiko Abe, et al.*2023 · 17 citations*](https://md.research.elamur.ai/free/en/papers/ando-2023-evaluating-large-language-models-neubaroco/)
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93. **[AICOE-Tredence at SemEval-2026 Task 11: Mitigating Content Bias in Syllogisms via Symbolic Logic-Language Decoupling](https://md.research.elamur.ai/free/en/papers/r-2026-aicoe-tredence-semeval-2026-task/) [International Workshop on Semantic Evaluation (SemEval)](https://md.research.elamur.ai/free/en/venues/semeval/)**Rakshith R, Ankush Chopra*2026 · 1 citation*
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95. **[Chain-of-Thought as a Lens: Evaluating Structured Reasoning Alignment between Human Preferences and Large Language Models](https://md.research.elamur.ai/free/en/papers/wang-2026-chain-thought-as-lens-evaluating/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/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 Reasoning](https://md.research.elamur.ai/free/en/papers/memar-2026-ellat-semeval-2026-task-11/) [International Workshop on Semantic Evaluation (SemEval)](https://md.research.elamur.ai/free/en/venues/semeval/)**Farzaneh Bayan Memar, Hanneke Huls, Matthijs Ten Hove*2026 · 1 citation*
97. **[GIL-Zaragoza at SemEval 2026 Task 11: Comparing Classification, Autoformalization, and Ontologies for Formal Reasoning Capabilities](https://md.research.elamur.ai/free/en/papers/ponce-2026-gil-zaragoza-semeval-2026-task/) [International Workshop on Semantic Evaluation (SemEval)](https://md.research.elamur.ai/free/en/venues/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 Optimization](https://md.research.elamur.ai/free/en/papers/shaikh-2026-habib-taz-semeval-2026-task/) [International Workshop on Semantic Evaluation (SemEval)](https://md.research.elamur.ai/free/en/venues/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 Classification](https://md.research.elamur.ai/free/en/papers/takahashi-2026-hidetsune-semeval-2026-task-11/) [International Workshop on Semantic Evaluation (SemEval)](https://md.research.elamur.ai/free/en/venues/semeval/)**Hidetsune Takahashi*2026 · 1 citation*
100. **[ITLC at SemEval-2026 Task 11: Normalization and Deterministic Parsing for Formal Reasoning in LLMs](https://md.research.elamur.ai/free/en/papers/muhamad-2026-itlc-semeval-2026-task-11/) [International Workshop on Semantic Evaluation (SemEval)](https://md.research.elamur.ai/free/en/venues/semeval/)**Wicaksono Leksono Muhamad, Joanito Agili Lopo, Tack Hwa Wong, et al.*2026 · 1 citation*

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