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Who cites «Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge»

1933 papers of our library cite it (of the 149,439 whose bibliographies we read) · 491 with a page here, the most cited first · page 1 of 5 · 1–100 · ← back to «Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge»

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  1. Language Models are Few-Shot LearnersarXiv:2005.14165Tom B. Brown, Benjamin Mann, Nick Ryder, et al.2020 · 14,178 citations
  2. Llama 2: Open Foundation and Fine-Tuned Chat ModelsarXiv:2307.09288Hugo Touvron, Louis Martin, Kevin Stone, et al.2023 · 9,669 citations
  3. LLaMA: Open and Efficient Foundation Language ModelsarXiv:2302.13971Hugo Touvron, Thibaut Lavril, Gautier Izacard, et al.2023 · 9,603 citations
  4. The Llama 3 Herd of ModelsarXiv:2407.21783Aaron Grattafiori, Abhimanyu Dubey, Abhinav Jauhri, et al.2024 · 7,682 citations
  5. PaLM: Scaling Language Modeling with PathwaysarXiv:2204.02311Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, et al.2022 · 4,753 citations
  6. Judging LLM-as-a-Judge with MT-Bench and Chatbot ArenaConference on Neural Information Processing Systems (NeurIPS)Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, et al.2023 · 4,573 citations
  7. Measuring Massive Multitask Language UnderstandingarXiv:2009.03300Dan Hendrycks, Collin Burns, Steven Basart, et al.2020 · 3,620 citations
  8. Self-Consistency Improves Chain of Thought Reasoning in Language ModelsarXiv:2203.11171Xuezhi Wang, Jason Wei, Dale Schuurmans, et al.2022 · 3,121 citations
  9. A Survey of Large Language ModelsFrontiers of Computer ScienceWayne Xin Zhao, Kun Zhou, Junyi Li, et al.2023 · 2,875 citations
  10. GPT-4o System CardarXiv:2410.21276OpenAI, Aaron Hurst, Adam Lerer, et al.2024 · 2,304 citations
  11. TruthfulQA: Measuring How Models Mimic Human FalsehoodsAnnual Meeting of the Association for Computational Linguistics (ACL)Stephanie Lin, Jacob Hilton, Owain Evans2022 · 2,088 citations
  12. Finetuned Language Models Are Zero-Shot LearnersarXiv:2109.01652Jason Wei, Maarten Bosma, Vincent Y. Zhao, et al.2021 · 1,954 citations
  13. 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 · 1,756 citations
  14. OPT: Open Pre-trained Transformer Language ModelsarXiv:2205.01068Susan Zhang, Stephen Roller, Naman Goyal, et al.2022 · 1,755 citations
  15. Qwen Technical ReportarXiv:2309.16609Jinze Bai, Shuai Bai, Yunfei Chu, et al.2023 · 1,669 citations
  16. Retrieval-Augmented Generation for Large Language Models: A SurveyarXiv:2312.10997Yunfan Gao, Yun Xiong, Xinyu Gao, et al.2023 · 1,663 citations
  17. Multitask Prompted Training Enables Zero-Shot Task GeneralizationarXiv:2110.08207Victor Sanh, Albert Webson, Colin Raffel, et al.2021 · 1,612 citations
  18. 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 · 1,444 citations
  19. A Comprehensive Overview of Large Language ModelsACM Transactions on Intelligent Systems and TechnologyHumza Naveed, Asad Ullah Khan, Shi Qiu, et al.2023 · 1,287 citations
  20. MS MARCO: A Human Generated MAchine Reading COmprehension DatasetarXiv:1611.09268Payal Bajaj, Daniel Campos, Nick Craswell, et al.2016 · 1,138 citations
  21. Phi-3 Technical Report: A Highly Capable Language Model Locally on Your PhonearXiv:2404.14219Marah Abdin, Jyoti Aneja, Hany Awadalla, et al.2024 · 1,134 citations
  22. PaLM 2 Technical ReportarXiv:2305.10403Rohan Anil, Andrew M. Dai, Orhan Firat, et al.2023 · 1,000 citations
  23. A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and InteractivityarXiv:2302.04023Yejin Bang, Samuel Cahyawijaya, Nayeon Lee, et al.2023 · 882 citations
  24. UNIFIEDQA: Crossing Format Boundaries with a Single QA SystemFindings of the Association for Computational LinguisticsDaniel Khashabi, Sewon Min, Tushar Khot, et al.2020 · 857 citations
  25. GPT-NeoX-20B: An Open-Source Autoregressive Language ModelBigScience Episode – Workshop on Challenges & Perspectives in Creating Large Language ModelsSidney Black, Stella Biderman, Eric Hallahan, et al.2022 · 831 citations
  26. Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and BeyondACM Transactions on Knowledge Discovery from DataJingfeng Yang, Hongye Jin, Ruixiang Tang, et al.2023 · 814 citations
  27. GLM-130B: An Open Bilingual Pre-trained ModelarXiv:2210.02414Aohan Zeng, Xiao Liu, Zhengxiao Du, et al.2022 · 786 citations
  28. Towards Reasoning in Large Language Models: A SurveyFindings of the Association for Computational LinguisticsJie Huang, Kevin Chen-Chuan Chang2023 · 780 citations
  29. MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding BenchmarkConference on Neural Information Processing Systems (NeurIPS)Yubo Wang, Xueguang Ma, Ge Zhang, et al.2024 · 761 citations
  30. WizardLM: Empowering large pre-trained language models to follow complex instructionsInternational Conference on Learning Representations (ICLR)Can Xu, Qingfeng Sun, Kai Zheng, et al.2023 · 741 citations
  31. KILT: a Benchmark for Knowledge Intensive Language TasksConference of the North American Chapter of the Association for Computational Linguistics (NAACL)Fabio Petroni, Aleksandra Piktus, Angela Fan, et al.2020 · 678 citations
  32. Self-RAG: Learning to Retrieve, Generate, and Critique through Self-ReflectionarXiv:2310.11511Akari Asai, Zeqiu Wu, Yizhong Wang, et al.2023 · 677 citations
  33. Atlas: Few-shot Learning with Retrieval Augmented Language ModelsarXiv:2208.03299Gautier Izacard, Patrick Lewis, Maria Lomeli, et al.2022 · 673 citations
  34. BloombergGPT: A Large Language Model for FinancearXiv:2303.17564Shijie Wu, Ozan Irsoy, Steven Lu, et al.2023 · 618 citations
  35. The Flan Collection: Designing Data and Methods for Effective Instruction TuningarXiv:2301.13688Shayne Longpre, Le Hou, Tu Vu, et al.2023 · 607 citations
  36. 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
  37. What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical ExamsarXiv:2009.13081Di Jin, Eileen Pan, Nassim Oufattole, et al.2020 · 567 citations
  38. Qwen2.5-Coder Technical ReportarXiv:2409.12186Binyuan Hui, Jian Yang, Zeyu Cui, et al.2024 · 559 citations
  39. 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
  40. Instruction Tuning for Large Language Models: A SurveyACM Computing SurveysShengyu Zhang, Linfeng Dong, Xiaoya Li, et al.2023 · 520 citations
  41. Few-shot Learning with Multilingual Language ModelsarXiv:2112.10668Xi Victoria Lin, Todor Mihaylov, Mikel Artetxe, et al.2021 · 500 citations
  42. BBQ: A hand-built bias benchmark for question answeringFindings of the Association for Computational LinguisticsAlicia Parrish, Angelica Chen, Nikita Nangia, et al.2022 · 497 citations
  43. GLaM: Efficient Scaling of Language Models with Mixture-of-ExpertsarXiv:2112.06905Nan Du, Yanping Huang, Andrew M. Dai, et al.2021 · 496 citations
  44. Large Language Models: A SurveyarXiv:2402.06196Shervin Minaee, Tomas Mikolov, Narjes Nikzad, et al.2024 · 471 citations
  45. LLM-Pruner: On the Structural Pruning of Large Language ModelsConference on Neural Information Processing Systems (NeurIPS)Xinyin Ma, Gongfan Fang, Xinchao Wang2023 · 452 citations
  46. FreeLB: Enhanced Adversarial Training for Natural Language UnderstandingarXiv:1909.11764Chen Zhu, Yu Cheng, Zhe Gan, et al.2019 · 452 citations
  47. The Falcon Series of Open Language ModelsarXiv:2311.16867Ebtesam Almazrouei, Hamza Alobeidli, Abdulaziz Alshamsi, et al.2023 · 443 citations
  48. 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 · 434 citations
  49. Yi: Open Foundation Models by 01.AIarXiv:2403.04652Alex Young, Bei Chen, Chao Li, et al.2024 · 408 citations
  50. LLM-Adapters: An Adapter Family for Parameter-Efficient Fine-Tuning of Large Language ModelsConference on Empirical Methods in Natural Language Processing (EMNLP)Zhiqiang Hu, Lei Wang, Yihuai Lan, et al.2023 · 401 citations
  51. Representation Engineering: A Top-Down Approach to AI TransparencyarXiv:2310.01405Andy Zou, Long Phan, Sarah Chen, et al.2023 · 397 citations
  52. CMMLU: Measuring massive multitask language understanding in ChinesearXiv:2306.09212Haonan Li, Yixuan Zhang, Fajri Koto, et al.2023 · 387 citations
  53. TinyLlama: An Open-Source Small Language ModelarXiv:2401.02385Peiyuan Zhang, Guangtao Zeng, Tianduo Wang, et al.2024 · 352 citations
  54. Kimi K2: Open Agentic IntelligencearXiv:2507.20534Kimi Team, Yifan Bai, Yiping Bao, et al.2025 · 347 citations
  55. UL2: Unifying Language Learning ParadigmsarXiv:2205.05131Yi Tay, Mostafa Dehghani, Vinh Q. Tran, et al.2022 · 347 citations
  56. LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init AttentionarXiv:2303.16199Renrui Zhang, Jiaming Han, Chris Liu, et al.2023 · 333 citations
  57. Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement LearningarXiv:2503.09516Bowen Jin, Hansi Zeng, Zhenrui Yue, et al.2025 · 322 citations
  58. 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 · 307 citations
  59. 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 · 300 citations
  60. xLSTM: Extended Long Short-Term MemoryConference on Neural Information Processing Systems (NeurIPS)Maximilian Beck, Korbinian Pöppel, Markus Spanring, et al.2024 · 296 citations
  61. Baize: An Open-Source Chat Model with Parameter-Efficient Tuning on Self-Chat DataConference on Empirical Methods in Natural Language Processing (EMNLP)Canwen Xu, Daya Guo, Nan Duan, et al.2023 · 296 citations
  62. ReClor: A Reading Comprehension Dataset Requiring Logical ReasoningarXiv:2002.04326Weihao Yu, Zihang Jiang, Yanfei Dong, et al.2020 · 286 citations
  63. 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 · 277 citations
  64. LLM-QAT: Data-Free Quantization Aware Training for Large Language ModelsFindings of the Association for Computational LinguisticsZechun Liu, Barlas Oguz, Changsheng Zhao, et al.2024 · 276 citations
  65. PowerInfer: Fast Large Language Model Serving with a Consumer-grade GPUACM SIGOPS Symposium on Operating Systems Principles (SOSP)Yixin Song, Zeyu Mi, Haotong Xie, et al.2023 · 272 citations
  66. MiniCPM: Unveiling the Potential of Small Language Models with Scalable Training StrategiesarXiv:2404.06395Shengding Hu, Yuge Tu, Xu Han, et al.2024 · 269 citations
  67. The Effect of Sampling Temperature on Problem Solving in Large Language ModelsFindings of the Association for Computational LinguisticsMatthew Renze2024 · 263 citations
  68. Measuring Faithfulness in Chain-of-Thought ReasoningarXiv:2307.13702Tamera Lanham, Anna Chen, Ansh Radhakrishnan, et al.2023 · 262 citations
  69. RedPajama: an Open Dataset for Training Large Language ModelsConference on Neural Information Processing Systems (NeurIPS)Maurice Weber, Daniel Fu, Quentin Anthony, et al.2024 · 260 citations
  70. Why Can GPT Learn In-Context? Language Models Secretly Perform Gradient Descent as Meta-OptimizersFindings of the Association for Computational LinguisticsDamai Dai, Yutao Sun, Li Dong, et al.2023 · 257 citations
  71. QuaRot: Outlier-Free 4-Bit Inference in Rotated LLMsConference on Neural Information Processing Systems (NeurIPS)Saleh Ashkboos, Amirkeivan Mohtashami, Maximilian L. Croci, et al.2024 · 254 citations
  72. Aya Model: An Instruction Finetuned Open-Access Multilingual Language ModelarXiv:2402.07827Ahmet Üstün, Viraat Aryabumi, Zheng-Xin Yong, et al.2024 · 253 citations
  73. Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive SurveyarXiv:2403.14608Zeyu Han, Chao Gao, Jinyang Liu, et al.2024 · 240 citations
  74. How Context Affects Language Models' Factual PredictionsarXiv:2005.04611Fabio Petroni, Patrick Lewis, Aleksandra Piktus, et al.2020 · 234 citations
  75. Jais and Jais-chat: Arabic-Centric Foundation and Instruction-Tuned Open Generative Large Language ModelsarXiv:2308.16149Neha Sengupta, Sunil Kumar Sahu, Bokang Jia, et al.2023 · 218 citations
  76. Language Models are Super Mario: Absorbing Abilities from Homologous Models as a Free LuncharXiv:2311.03099Le Yu, Bowen Yu, Haiyang Yu, et al.2023 · 217 citations
  77. Molmo and PixMo: Open Weights and Open Data for State-of-the-Art Vision-Language ModelsIEEE Computer Society Conference on Computer Vision and Pattern RecognitionMatt Deitke, Christopher Clark, Sangho Lee, et al.2024 · 216 citations
  78. Compressing Context to Enhance Inference Efficiency of Large Language ModelsConference on Empirical Methods in Natural Language Processing (EMNLP)Yucheng Li, Bo Dong, Frank Guerin, et al.2023 · 212 citations
  79. Explaining Answers with Entailment TreesConference on Empirical Methods in Natural Language Processing (EMNLP)Bhavana Dalvi, Peter Jansen, Oyvind Tafjord, et al.2021 · 211 citations
  80. CrossFit: A Few-shot Learning Challenge for Cross-task Generalization in NLPConference on Empirical Methods in Natural Language Processing (EMNLP)Qinyuan Ye, Bill Yuchen Lin, Xiang Ren2021 · 210 citations
  81. A Systematic Study and Comprehensive Evaluation of ChatGPT on Benchmark DatasetsFindings of the Association for Computational LinguisticsMd Tahmid Rahman Laskar, M Saiful Bari, Mizanur Rahman, et al.2023 · 200 citations
  82. Generative Data Augmentation for Commonsense ReasoningFindings of the Association for Computational LinguisticsYiben Yang, Chaitanya Malaviya, Jared Fernandez, et al.2020 · 198 citations
  83. Towards Faithful Model Explanation in NLP: A SurveyComputational LinguisticsQing Lyu, Marianna Apidianaki, Chris Callison-Burch2024 · 197 citations
  84. DeepSeek-Coder-V2: Breaking the Barrier of Closed-Source Models in Code IntelligencearXiv:2406.11931DeepSeek-AI, Qihao Zhu, Daya Guo, et al.2024 · 196 citations
  85. Refusal in Language Models Is Mediated by a Single DirectionarXiv:2406.11717Andy Arditi, Oscar Obeso, Aaquib Syed, et al.2024 · 196 citations
  86. Okapi: Instruction-tuned Large Language Models in Multiple Languages with Reinforcement Learning from Human FeedbackConference on Empirical Methods in Natural Language Processing (EMNLP)Viet Lai, Chien Nguyen, Nghia Ngo, et al.2023 · 195 citations
  87. OmniQuant: Omnidirectionally Calibrated Quantization for Large Language ModelsarXiv:2308.13137Wenqi Shao, Mengzhao Chen, Zhaoyang Zhang, et al.2023 · 195 citations
  88. 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 · 191 citations
  89. 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
  90. Effective Long-Context Scaling of Foundation ModelsarXiv:2309.16039Wenhan Xiong, Jingyu Liu, Igor Molybog, et al.2023 · 184 citations
  91. Evaluating Large Language Models: A Comprehensive SurveyarXiv:2310.19736Zishan Guo, Renren Jin, Chuang Liu, et al.2023 · 182 citations
  92. SmolLM2: When Smol Goes Big — Data-Centric Training of a Small Language ModelarXiv:2502.02737Loubna Ben Allal, Anton Lozhkov, Elie Bakouch, et al.2025 · 181 citations
  93. What Makes Reading Comprehension Questions Easier?Conference on Empirical Methods in Natural Language Processing (EMNLP)Saku Sugawara, Kentaro Inui, Satoshi Sekine, et al.2018 · 179 citations
  94. Large Language Diffusion ModelsConference on Neural Information Processing Systems (NeurIPS)Shen Nie, Fengqi Zhu, Zebin You, et al.2025 · 178 citations
  95. What Makes Good Data for Alignment? A Comprehensive Study of Automatic Data Selection in Instruction TuningarXiv:2312.15685Wei Liu, Weihao Zeng, Keqing He, et al.2023 · 178 citations
  96. 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
  97. Improving Machine Reading Comprehension with General Reading StrategiesConference of the North American Chapter of the Association for Computational Linguistics (NAACL)Kai Sun, Dian Yu, Dong Yu, et al.2019 · 172 citations
  98. Survey on Factuality in Large Language Models: Knowledge, Retrieval and Domain-SpecificityarXiv:2310.07521Cunxiang Wang, Xiaoze Liu, Yuanhao Yue, et al.2023 · 171 citations
  99. Orca 2: Teaching Small Language Models How to ReasonarXiv:2311.11045Arindam Mitra, Luciano Del Corro, Shweti Mahajan, et al.2023 · 162 citations
  100. From Quantity to Quality: Boosting LLM Performance with Self-Guided Data Selection for Instruction TuningarXiv:2308.12032Ming Li, Yong Zhang, Zhitao Li, et al.2023 · 161 citations

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