🔗 Who cites «CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge»

1,148 papers of our library cite it (of the 141,840 whose bibliographies we read) · 141 with a page here, the most cited first · page 1 of 2 · 1–100 · ← back to «CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge»

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1. Chain-of-Thought Prompting Elicits Reasoning in Large Language ModelsarXiv:2201.11903Jason Wei, Xuezhi Wang, Dale Schuurmans, et al.2022 · 11,308 citations
2. PaLM: Scaling Language Modeling with PathwaysarXiv:2204.02311Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, et al.2022 · 4,680 citations
3. Large language models are zero-shot reasonersConference on Neural Information Processing Systems (NeurIPS)Takeshi Kojima, Shixiang Shane Gu, Machel Reid, et al.2022 · 3,795 citations
4. Training Verifiers to Solve Math Word ProblemsarXiv:2110.14168Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, et al.2021 · 3,680 citations
5. Self-Consistency Improves Chain of Thought Reasoning in Language ModelsarXiv:2203.11171Xuezhi Wang, Jason Wei, Dale Schuurmans, et al.2022 · 3,041 citations
6. Language Models as Knowledge Bases?Conference on Empirical Methods in Natural Language Processing (EMNLP)Fabio Petroni, Tim Rocktäschel, Sebastian Riedel, et al.2019 · 2,992 citations
7. A Survey of Large Language ModelsFrontiers of Computer ScienceWayne Xin Zhao, Kun Zhou, Junyi Li, et al.2023 · 2,863 citations
8. TruthfulQA: Measuring How Models Mimic Human FalsehoodsAnnual Meeting of the Association for Computational Linguistics (ACL)Stephanie Lin, Jacob Hilton, Owain Evans2022 · 2,052 citations
9. Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language modelsarXiv:2206.04615 · Transactions on Machine Learning Research, May/2022, https://openreview.net/forum?id=uyTL5BvosjAarohi Srivastava, Abhinav Rastogi, Abhishek Rao, et al.2022 · 1,703 citations
10. Retrieval-Augmented Generation for Large Language Models: A SurveyarXiv:2312.10997Yunfan Gao, Yun Xiong, Xinyu Gao, et al.2023 · 1,648 citations
11. Qwen Technical ReportarXiv:2309.16609Jinze Bai, Shuai Bai, Yunfei Chu, et al.2023 · 1,645 citations
12. Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?Conference on Empirical Methods in Natural Language Processing (EMNLP)Sewon Min, Xinxi Lyu, Ari Holtzman, et al.2022 · 1,425 citations
13. A Comprehensive Overview of Large Language ModelsACM Transactions on Intelligent Systems and TechnologyHumza Naveed, Asad Ullah Khan, Shi Qiu, et al.2023 · 1,286 citations
14. Phi-3 Technical Report: A Highly Capable Language Model Locally on Your PhonearXiv:2404.14219Marah Abdin, Jyoti Aneja, Hany Awadalla, et al.2024 · 1,113 citations
15. PaLM 2 Technical ReportarXiv:2305.10403Rohan Anil, Andrew M. Dai, Orhan Firat, et al.2023 · 977 citations
16. GPQA: A Graduate-Level Google-Proof Q&A BenchmarkarXiv:2311.12022David Rein, Betty Li Hou, Asa Cooper Stickland, et al.2023 · 964 citations
17. A Survey on In-context LearningarXiv:2301.00234Qingxiu Dong, Lei Li, Damai Dai, et al.2023 · 959 citations
18. AdapterFusion: Non-Destructive Task Composition for Transfer LearningarXiv:2005.00247 · Proceedings of EACL 2021Jonas Pfeiffer, Aishwarya Kamath, Andreas Rücklé, et al.2020 · 950 citations
19. A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and InteractivityarXiv:2302.04023Yejin Bang, Samuel Cahyawijaya, Nayeon Lee, et al.2023 · 876 citations
20. UNIFIEDQA: Crossing Format Boundaries with a Single QA SystemFindings of the Association for Computational LinguisticsDaniel Khashabi, Sewon Min, Tushar Khot, et al.2020 · 844 citations
21. Is ChatGPT a General-Purpose Natural Language Processing Task Solver?Conference on Empirical Methods in Natural Language Processing (EMNLP)Chengwei Qin, Aston Zhang, Zhuosheng Zhang, et al.2023 · 823 citations
22. ERASER: A Benchmark to Evaluate Rationalized NLP ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Jay DeYoung, Sarthak Jain, Nazneen Fatema Rajani, et al.2020 · 821 citations
23. GLM-130B: An Open Bilingual Pre-trained ModelarXiv:2210.02414Aohan Zeng, Xiao Liu, Zhengxiao Du, et al.2022 · 779 citations
24. Towards Reasoning in Large Language Models: A SurveyFindings of the Association for Computational LinguisticsJie Huang, Kevin Chen-Chuan Chang2023 · 778 citations
25. STaR: Bootstrapping Reasoning With ReasoningEric Zelikman, Yuhuai Wu, Jesse Mu, et al.2022 · 743 citations
26. Explain Yourself! Leveraging Language Models for Commonsense ReasoningAnnual Meeting of the Association for Computational Linguistics (ACL)Nazneen Fatema Rajani, Bryan McCann, Caiming Xiong, et al.2019 · 693 citations
27. Automatic Chain of Thought Prompting in Large Language ModelsarXiv:2210.03493Zhuosheng Zhang, Aston Zhang, Mu Li, et al.2022 · 690 citations
28. Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model SizesFindings of the Association for Computational LinguisticsCheng-Yu Hsieh, Chun-Liang Li, Chih-kuan Yeh, et al.2023 · 685 citations
29. BloombergGPT: A Large Language Model for FinancearXiv:2303.17564Shijie Wu, Ozan Irsoy, Steven Lu, et al.2023 · 612 citations
30. The Flan Collection: Designing Data and Methods for Effective Instruction TuningarXiv:2301.13688Shayne Longpre, Le Hou, Tu Vu, et al.2023 · 592 citations
31. Galactica: A Large Language Model for SciencearXiv:2211.09085Ross Taylor, Marcin Kardas, Guillem Cucurull, et al.2022 · 565 citations
32. MetaMath: Bootstrap Your Own Mathematical Questions for Large Language ModelsarXiv:2309.12284Longhui Yu, Weisen Jiang, Han Shi, et al.2023 · 556 citations
33. Cosmos QA: Machine Reading Comprehension with Contextual Commonsense ReasoningLifu Huang, Ronan Le Bras, Chandra Bhagavatula, et al.2019 · 539 citations
34. Unleashing the potential of prompt engineering for large language modelsarXiv:2310.14735 · Patterns 6(6) 101,260 (2025)Banghao Chen, Zhaofeng Zhang, Nicolas Langrene, et al.2023 · 536 citations
35. A-OKVQA: A Benchmark for Visual Question Answering using World KnowledgeLecture notes in computer scienceDustin Schwenk, Apoorv Khandelwal, Christopher Clark, et al.2022 · 490 citations
36. XCOPA: A Multilingual Dataset for Causal Commonsense ReasoningConference on Empirical Methods in Natural Language Processing (EMNLP)Edoardo Maria Ponti, Goran Glavaš, Olga Majewska, et al.2020 · 465 citations
37. FreeLB: Enhanced Adversarial Training for Natural Language UnderstandingarXiv:1909.11764Chen Zhu, Yu Cheng, Zhe Gan, et al.2019 · 446 citations
38. oLMpics — On what Language Model Pre-training CapturesTransactions of the Association for Computational Linguistics (TACL)Alon Talmor, Yanai Elazar, Yoav Goldberg, et al.2019 · 435 citations
39. ChatGPT Beyond English: Towards a Comprehensive Evaluation of Large Language Models in Multilingual LearningFindings of the Association for Computational LinguisticsViet Dac Lai, Nghia Ngo, Amir Pouran Ben Veyseh, et al.2023 · 421 citations
40. AgentBench: Evaluating LLMs as AgentsarXiv:2308.03688Xiao Liu, Hao Yu, Hanchen Zhang, et al.2023 · 405 citations
41. The FineWeb Datasets: Decanting the Web for the Finest Text Data at ScaleConference on Neural Information Processing Systems (NeurIPS)Guilherme Penedo, Hynek Kydlíček, Loubna Ben allal, et al.2024 · 404 citations
42. Yi: Open Foundation Models by 01.AIarXiv:2403.04652Alex Young, Bei Chen, Chao Li, et al.2024 · 402 citations
43. Large Language Models Cannot Self-Correct Reasoning YetarXiv:2310.01798Jie Huang, Xinyun Chen, Swaroop Mishra, et al.2023 · 396 citations
44. Representation Engineering: A Top-Down Approach to AI TransparencyarXiv:2310.01405Andy Zou, Long Phan, Sarah Chen, et al.2023 · 386 citations
45. CMMLU: Measuring massive multitask language understanding in ChinesearXiv:2306.09212Haonan Li, Yixuan Zhang, Fajri Koto, et al.2023 · 385 citations
46. 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
47. WizardMath: Empowering Mathematical Reasoning for Large Language Models via Reinforced Evol-InstructarXiv:2308.09583Haipeng Luo, Qingfeng Sun, Can Xu, et al.2023 · 366 citations
48. The AI Scientist: Towards Fully Automated Open-Ended Scientific DiscoveryarXiv:2408.06292Chris Lu, Cong Lu, Robert Tjarko Lange, et al.2024 · 362 citations
49. UL2: Unifying Language Learning ParadigmsarXiv:2205.05131Yi Tay, Mostafa Dehghani, Vinh Q. Tran, et al.2022 · 335 citations
50. WANLI: Worker and AI Collaboration for Natural Language Inference Dataset CreationarXiv:2201.05955Alisa Liu, Swabha Swayamdipta, Noah A. Smith, et al.2022 · 314 citations
51. Large Language Models are Better Reasoners with Self-VerificationFindings of the Association for Computational LinguisticsYixuan Weng, Minjun Zhu, Fei Xia, et al.2023 · 302 citations
52. Muppet: Massive Multi-task Representations with Pre-FinetuningConference on Empirical Methods in Natural Language Processing (EMNLP)Armen Aghajanyan, Anchit Gupta, Akshat Shrivastava, et al.2021 · 299 citations
53. Scalable Multi-Hop Relational Reasoning for Knowledge-Aware Question AnsweringConference on Empirical Methods in Natural Language Processing (EMNLP)Yanlin Feng, Xinyue Chen, Bill Yuchen Lin, et al.2020 · 296 citations
54. Towards Debiasing Fact Verification ModelsConference on Empirical Methods in Natural Language Processing (EMNLP)Tal Schuster, Darsh Shah, Yun Jie Serene Yeo, et al.2019 · 296 citations
55. Intermediate-Task Transfer Learning with Pretrained Language Models: When and Why Does It Work?Annual Meeting of the Association for Computational Linguistics (ACL)Yada Pruksachatkun, Jason Phang, Haokun Liu, et al.2020 · 281 citations
56. Surface Form Competition: Why the Highest Probability Answer Isn't Always RightConference on Empirical Methods in Natural Language Processing (EMNLP)Ari Holtzman, Peter West, Vered Shwartz, et al.2021 · 271 citations
57. Large Language Models Sensitivity to The Order of Options in Multiple-Choice QuestionsFindings of the Association for Computational LinguisticsPouya Pezeshkpour, Estevam Hruschka2023 · 256 citations
58. SocialIQA: Commonsense Reasoning about Social InteractionsarXiv:1904.09728Maarten Sap, Hannah Rashkin, Derek Chen, et al.2019 · 246 citations
59. Language Models can Solve Computer TasksConference on Neural Information Processing Systems (NeurIPS)Geunwoo Kim, Pierre Baldi, Stephen McAleer2023 · 239 citations
60. FOLIO: Natural Language Reasoning with First-Order LogicarXiv:2209.00840Simeng Han, Hailey Schoelkopf, Yilun Zhao, et al.2022 · 217 citations
61. Birds have four legs?! NumerSense: Probing Numerical Commonsense Knowledge of Pre-Trained Language ModelsConference on Empirical Methods in Natural Language Processing (EMNLP)Bill Yuchen Lin, Seyeon Lee, Rahul Khanna, et al.2020 · 217 citations
62. 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 · 216 citations
63. Measuring Association Between Labels and Free-Text RationalesConference on Empirical Methods in Natural Language Processing (EMNLP)Sarah Wiegreffe, Ana Marasović, Noah A. Smith2021 · 215 citations
64. PlanBench: An Extensible Benchmark for Evaluating Large Language Models on Planning and Reasoning about ChangeConference on Neural Information Processing Systems (NeurIPS)Karthik Valmeekam, Matthew Marquez, Alberto Olmo, et al.2022 · 209 citations
65. Logical Natural Language Generation from Open-Domain TablesAnnual Meeting of the Association for Computational Linguistics (ACL)Wenhu Chen, Jianshu Chen, Yu Su, et al.2020 · 209 citations
66. Aligning Large Language Models with Human: A SurveyarXiv:2307.12966Yufei Wang, Wanjun Zhong, Liangyou Li, et al.2023 · 199 citations
67. Generative Data Augmentation for Commonsense ReasoningFindings of the Association for Computational LinguisticsYiben Yang, Chaitanya Malaviya, Jared Fernandez, et al.2020 · 195 citations
68. What Can We Learn from Collective Human Opinions on Natural Language Inference Data?Conference on Empirical Methods in Natural Language Processing (EMNLP)Yixin Nie, Xiang Zhou, Mohit Bansal2020 · 193 citations
69. MuTual: A Dataset for Multi-Turn Dialogue ReasoningAnnual Meeting of the Association for Computational Linguistics (ACL)Leyang Cui, Yu Wu, Shujie Liu, et al.2020 · 190 citations
70. Evaluating Large Language Models: A Comprehensive SurveyarXiv:2310.19736Zishan Guo, Renren Jin, Chuang Liu, et al.2023 · 178 citations
71. An Information-theoretic Approach to Prompt Engineering Without Ground Truth LabelsAnnual Meeting of the Association for Computational Linguistics (ACL)Taylor Sorensen, Joshua Robinson, Christopher Rytting, et al.2022 · 178 citations
72. Effective Long-Context Scaling of Foundation ModelsarXiv:2309.16039Wenhan Xiong, Jingyu Liu, Igor Molybog, et al.2023 · 177 citations
73. RealTime QA: What's the Answer Right Now?Conference on Neural Information Processing Systems (NeurIPS)Jungo Kasai, Keisuke Sakaguchi, Yoichi Takahashi, et al.2022 · 176 citations
74. Does it Make Sense? And Why? A Pilot Study for Sense Making and ExplanationAnnual Meeting of the Association for Computational Linguistics (ACL)Cunxiang Wang, Shuailong Liang, Yue Zhang, et al.2019 · 174 citations
75. SmolLM2: When Smol Goes Big — Data-Centric Training of a Small Language ModelarXiv:2502.02737Loubna Ben Allal, Anton Lozhkov, Elie Bakouch, et al.2025 · 171 citations
76. Promptbreeder: Self-Referential Self-Improvement Via Prompt EvolutionarXiv:2309.16797Chrisantha Fernando, Dylan Banarse, Henryk Michalewski, et al.2023 · 165 citations
77. Explanations for CommonsenseQA: New Dataset and ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Shourya Aggarwal, Divyanshu Mandowara, Vishwajeet Agrawal, et al.2021 · 163 citations
78. Reasoning with Language Model Prompting: A SurveyarXiv:2212.09597Shuofei Qiao, Yixin Ou, Ningyu Zhang, et al.2022 · 161 citations
79. Leveraging Large Language Models for Multiple Choice Question AnsweringarXiv:2210.12353Joshua Robinson, Christopher Michael Rytting, David Wingate2022 · 157 citations
80. SemEval-2020 Task 4: Commonsense Validation and ExplanationInternational Workshop on Semantic Evaluation (SemEval)Cunxiang Wang, Shuailong Liang, Yili Jin, et al.2020 · 157 citations
81. DataComp-LM: In search of the next generation of training sets for language modelsConference on Neural Information Processing Systems (NeurIPS)Jeffrey Li, Alex Fang, Georgios Smyrnis, et al.2024 · 156 citations
82. ReConcile: Round-Table Conference Improves Reasoning via Consensus among Diverse LLMsAnnual Meeting of the Association for Computational Linguistics (ACL)Justin Chen, Swarnadeep Saha, Mohit Bansal2024 · 154 citations
83. Explaining NLP Models via Minimal Contrastive Editing (MiCE)Findings of the Association for Computational LinguisticsAlexis Ross, Ana Marasović, Matthew Peters2021 · 154 citations
84. INFOTABS: Inference on Tables as Semi-structured DataAnnual Meeting of the Association for Computational Linguistics (ACL)Vivek Gupta, Maitrey Mehta, Pegah Nokhiz, et al.2020 · 150 citations
85. RA-DIT: Retrieval-Augmented Dual Instruction TuningarXiv:2310.01352Xi Victoria Lin, Xilun Chen, Mingda Chen, et al.2023 · 145 citations
86. SCOTT: Self-Consistent Chain-of-Thought DistillationAnnual Meeting of the Association for Computational Linguistics (ACL)Peifeng Wang, Zhengyang Wang, Zheng Li, et al.2023 · 145 citations
87. Self-Evaluation Guided Beam Search for ReasoningConference on Neural Information Processing Systems (NeurIPS)Yuxi Xie, Kenji Kawaguchi, Yiran Zhao, et al.2023 · 145 citations
88. GLUCOSE: GeneraLized and COntextualized Story ExplanationsNasrin Mostafazadeh, Aditya Kalyanpur, Lori Moon, et al.2020 · 144 citations
89. Justice or Prejudice? Quantifying Biases in LLM-as-a-JudgearXiv:2410.02736Jiayi Ye, Yanbo Wang, Yue Huang, et al.2024 · 142 citations
90. Knowledgeable or Educated Guess? Revisiting Language Models as Knowledge BasesAnnual Meeting of the Association for Computational Linguistics (ACL)Boxi Cao, Hongyu Lin, Xianpei Han, et al.2021 · 141 citations
91. Symbolic Chain-of-Thought Distillation: Small Models Can Also “Think” Step-by-StepAnnual Meeting of the Association for Computational Linguistics (ACL)Liunian Harold Li, Jack Hessel, Youngjae Yu, et al.2023 · 139 citations
92. Self-Knowledge Guided Retrieval Augmentation for Large Language ModelsFindings of the Association for Computational LinguisticsYile Wang, Peng Li, Maosong Sun, et al.2023 · 135 citations
93. Recent Advances in Natural Language Inference: A Survey of Benchmarks, Resources, and ApproachesarXiv:1904.01172Shane Storks, Qiaozi Gao, Joyce Y. Chai2019 · 134 citations
94. To CoT or not to CoT? Chain-of-thought helps mainly on math and symbolic reasoningarXiv:2409.12183Zayne Sprague, Fangcong Yin, Juan Diego Rodriguez, et al.2024 · 133 citations
95. What to Pre-Train on? Efficient Intermediate Task SelectionConference on Empirical Methods in Natural Language Processing (EMNLP)Clifton Poth, Jonas Pfeiffer, Andreas Rücklé, et al.2021 · 132 citations
96. 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
97. Continual Pre-Training for Cross-Lingual LLM Adaptation: Enhancing Japanese Language CapabilitiesarXiv:2404.17790Kazuki Fujii, Taishi Nakamura, Mengsay Loem, et al.2024 · 114 citations
98. SALAD-Bench: A Hierarchical and Comprehensive Safety Benchmark for Large Language ModelsFindings of the Association for Computational LinguisticsLijun Li, Bowen Dong, Ruohui Wang, et al.2024 · 114 citations
99. Quiet-STaR: Language models can teach themselves to think before speaking2024 · 113 citations
100. Olmo 3arXiv:2512.13961Team Olmo: Allyson Ettinger, Amanda Bertsch, Bailey Kuehl, et al.2025 · 109 citations

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