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Who cites «Finetuned Language Models Are Zero-Shot Learners»

1,887 papers of our library cite it (of the 149,439 whose bibliographies we read) · 499 with a page here, the most cited first · page 2 of 5 · 101–200 · ← back to «Finetuned Language Models Are Zero-Shot Learners»

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  1. MEGA: Multilingual Evaluation of Generative AIarXiv:2303.12528Kabir Ahuja, Harshita Diddee, Rishav Hada, et al.2023 · 285 citations
  2. A Comprehensive Capability Analysis of GPT-3 and GPT-3.5 Series ModelsarXiv:2303.10420Junjie Ye, Xuanting Chen, Nuo Xu, et al.2023 · 283 citations
  3. Generate rather than Retrieve: Large Language Models are Strong Context GeneratorsarXiv:2209.10063Wenhao Yu, Dan Iter, Shuohang Wang, et al.2022 · 281 citations
  4. 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
  5. MiniCPM: Unveiling the Potential of Small Language Models with Scalable Training StrategiesarXiv:2404.06395Shengding Hu, Yuge Tu, Xu Han, et al.2024 · 269 citations
  6. Large Language Models Are Zero-Shot Time Series ForecastersConference on Neural Information Processing Systems (NeurIPS)Nate Gruver, Marc Finzi, Shikai Qiu, et al.2023 · 268 citations
  7. Predictability and Surprise in Large Generative ModelsACM Conference on Fairness, Accountability, and Transparency (FAccT)Deep Ganguli, Danny Hernandez, Liane Lovitt, et al.2022 · 268 citations
  8. A General Theoretical Paradigm to Understand Learning from Human PreferencesarXiv:2310.12036Mohammad Gheshlaghi Azar, Mark Rowland, Bilal Piot, et al.2023 · 263 citations
  9. LLaMA-VID: An Image is Worth 2 Tokens in Large Language ModelsLecture notes in computer scienceYanwei Li, Chengyao Wang, Jiaya Jia2023 · 262 citations
  10. Scaling Relationship on Learning Mathematical Reasoning with Large Language ModelsarXiv:2308.01825Zheng Yuan, Hongyi Yuan, Chengpeng Li, et al.2023 · 261 citations
  11. DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT ModelsConference on Neural Information Processing Systems (NeurIPS)Boxin Wang, Weixin Chen, Hengzhi Pei, et al.2023 · 259 citations
  12. Aya Model: An Instruction Finetuned Open-Access Multilingual Language ModelarXiv:2402.07827Ahmet Üstün, Viraat Aryabumi, Zheng-Xin Yong, et al.2024 · 253 citations
  13. Personality Traits in Large Language ModelsarXiv:2307.00184Greg Serapio-Garcia, Mustafa Safdari, Clement Crepy, et al.2023 · 252 citations
  14. RAFT: Reward rAnked FineTuning for Generative Foundation Model AlignmentarXiv:2304.06767Hanze Dong, Wei Xiong, Deepanshu Goyal, et al.2023 · 252 citations
  15. NLP Evaluation in trouble: On the Need to Measure LLM Data Contamination for each BenchmarkFindings of the Association for Computational LinguisticsOscar Sainz, Jon Campos, Iker García-Ferrero, et al.2023 · 250 citations
  16. Language Models can Solve Computer TasksConference on Neural Information Processing Systems (NeurIPS)Geunwoo Kim, Pierre Baldi, Stephen McAleer2023 · 249 citations
  17. ZeroGen: Efficient Zero-shot Learning via Dataset GenerationConference on Empirical Methods in Natural Language Processing (EMNLP)Jiacheng Ye, Jiahui Gao, Qintong Li, et al.2022 · 245 citations
  18. OPT-IML: Scaling Language Model Instruction Meta Learning through the Lens of GeneralizationarXiv:2212.12017Srinivasan Iyer, Xi Victoria Lin, Ramakanth Pasunuru, et al.2022 · 244 citations
  19. Synthetic Data Generation with Large Language Models for Text Classification: Potential and LimitationsConference on Empirical Methods in Natural Language Processing (EMNLP)Zhuoyan Li, Hangxiao Zhu, Zhuoran Lu, et al.2023 · 242 citations
  20. Reframing Instructional Prompts to GPTk's LanguageFindings of the Association for Computational LinguisticsSwaroop Mishra, Daniel Khashabi, Chitta Baral, et al.2022 · 242 citations
  21. Visual Adversarial Examples Jailbreak Aligned Large Language ModelsProceedings of the AAAI Conference on Artificial IntelligenceXiangyu Qi, Kaixuan Huang, Ashwinee Panda, et al.2023 · 240 citations
  22. Detecting and Preventing Hallucinations in Large Vision Language ModelsProceedings of the AAAI Conference on Artificial IntelligenceAnisha Gunjal, Jihan Yin, Erhan Bas2023 · 235 citations
  23. LLaMA-Adapter V2: Parameter-Efficient Visual Instruction ModelarXiv:2304.15010Peng Gao, Jiaming Han, Renrui Zhang, et al.2023 · 229 citations
  24. Tower: An Open Multilingual Large Language Model for Translation-Related TasksarXiv:2402.17733Duarte M. Alves, Jose Pombal, Nuno M. Guerreiro, et al.2024 · 227 citations
  25. How Language Model Hallucinations Can SnowballarXiv:2305.13534Muru Zhang, Ofir Press, William Merrill, et al.2023 · 226 citations
  26. Not All Languages Are Created Equal in LLMs: Improving Multilingual Capability by Cross-Lingual-Thought PromptingarXiv:2305.07004Haoyang Huang, Tianyi Tang, Dongdong Zhang, et al.2023 · 224 citations
  27. NV-Embed: Improved Techniques for Training LLMs as Generalist Embedding ModelsarXiv:2405.17428Chankyu Lee, Rajarshi Roy, Mengyao Xu, et al.2024 · 222 citations
  28. Med-HALT: Medical Domain Hallucination Test for Large Language ModelsConference on Computational Natural Language Learning (CoNLL)Ankit Pal, Logesh Kumar Umapathi, Malaikannan Sankarasubbu2023 · 221 citations
  29. AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent BehaviorsarXiv:2308.10848Weize Chen, Yusheng Su, Jingwei Zuo, et al.2023 · 217 citations
  30. LLM+P: Empowering Large Language Models with Optimal Planning ProficiencyarXiv:2304.11477Bo Liu, Yuqian Jiang, Xiaohan Zhang, et al.2023 · 216 citations
  31. What matters when building vision-language models?Conference on Neural Information Processing Systems (NeurIPS)Hugo Laurençon, Leo Tronchon, Matthieu Cord, et al.2024 · 212 citations
  32. Large Language Model Is Not a Good Few-shot Information Extractor, but a Good Reranker for Hard Samples!Findings of the Association for Computational LinguisticsYubo Ma, Yixin Cao, Yong Hong, et al.2023 · 212 citations
  33. Generating Training Data with Language Models: Towards Zero-Shot Language UnderstandingarXiv:2202.04538Yu Meng, Jiaxin Huang, Yu Zhang, et al.2022 · 210 citations
  34. 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
  35. SALMONN: Towards Generic Hearing Abilities for Large Language ModelsarXiv:2310.13289Changli Tang, Wenyi Yu, Guangzhi Sun, et al.2023 · 209 citations
  36. Internet-augmented language models through few-shot prompting for open-domain question answeringarXiv:2203.05115Angeliki Lazaridou, Elena Gribovskaya, Wojciech Stokowiec, et al.2022 · 209 citations
  37. Aya Dataset: An Open-Access Collection for Multilingual Instruction TuningAnnual Meeting of the Association for Computational Linguistics (ACL)Shivalika Singh, Freddie Vargus, Daniel D'souza, et al.2024 · 207 citations
  38. Aligning Large Language Models with Human: A SurveyarXiv:2307.12966Yufei Wang, Wanjun Zhong, Liangyou Li, et al.2023 · 202 citations
  39. 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
  40. Improving Passage Retrieval with Zero-Shot Question GenerationConference on Empirical Methods in Natural Language Processing (EMNLP)Devendra Sachan, Mike Lewis, Mandar Joshi, et al.2022 · 199 citations
  41. 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
  42. One Embedder, Any Task: Instruction-Finetuned Text EmbeddingsarXiv:2212.09741Hongjin Su, Weijia Shi, Jungo Kasai, et al.2022 · 193 citations
  43. Do NOT Think That Much for 2+3=? On the Overthinking of o1-Like LLMsarXiv:2412.21187Xingyu Chen, Jiahao Xu, Tian Liang, et al.2024 · 192 citations
  44. On Second Thought, Let's Not Think Step by Step! Bias and Toxicity in Zero-Shot ReasoningAnnual Meeting of the Association for Computational Linguistics (ACL)Omar Shaikh, Hongxin Zhang, William Held, et al.2023 · 192 citations
  45. 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
  46. Towards Making the Most of ChatGPT for Machine TranslationFindings of the Association for Computational LinguisticsKeqin Peng, Liang Ding, Qihuang Zhong, et al.2023 · 190 citations
  47. The Prompt Report: A Systematic Survey of Prompt Engineering TechniquesarXiv:2406.06608Sander Schulhoff, Michael Ilie, Nishant Balepur, et al.2024 · 189 citations
  48. LawBench: Benchmarking Legal Knowledge of Large Language ModelsarXiv:2309.16289Zhiwei Fei, Xiaoyu Shen, Dawei Zhu, et al.2023 · 188 citations
  49. GrIPS: Gradient-free, Edit-based Instruction Search for Prompting Large Language ModelsConference of the European Chapter of the Association for Computational Linguistics (EACL)Archiki Prasad, Peter Hase, Xiang Zhou, et al.2023 · 184 citations
  50. 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
  51. LILA: A Unified Benchmark for Mathematical ReasoningConference on Empirical Methods in Natural Language Processing (EMNLP)Swaroop Mishra, Matthew Finlayson, Pan Lu, et al.2022 · 179 citations
  52. LESS: Selecting Influential Data for Targeted Instruction TuningarXiv:2402.04333Mengzhou Xia, Sadhika Malladi, Suchin Gururangan, et al.2024 · 178 citations
  53. Learning to Compress Prompts with Gist TokensConference on Neural Information Processing Systems (NeurIPS)Jesse Mu, Xiang Lisa Li, Noah Goodman2023 · 178 citations
  54. Aligning Large Multimodal Models with Factually Augmented RLHFarXiv:2309.14525Zhiqing Sun, Sheng Shen, Shengcao Cao, et al.2023 · 176 citations
  55. PromptSource: An Integrated Development Environment and Repository for Natural Language PromptsarXiv:2202.01279Stephen H. Bach, Victor Sanh, Zheng-Xin Yong, et al.2022 · 174 citations
  56. Generative Representational Instruction TuningarXiv:2402.09906Niklas Muennighoff, Hongjin Su, Liang Wang, et al.2024 · 172 citations
  57. Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak SupervisionarXiv:2312.09390Collin Burns, Pavel Izmailov, Jan Hendrik Kirchner, et al.2023 · 172 citations
  58. HuaTuo: Tuning LLaMA Model with Chinese Medical KnowledgearXiv:2304.06975Haochun Wang, Chi Liu, Nuwa Xi, et al.2023 · 171 citations
  59. Capabilities of Gemini Models in MedicinearXiv:2404.18416Khaled Saab, Tao Tu, Wei-Hung Weng, et al.2024 · 170 citations
  60. Promptagator: Few-shot Dense Retrieval From 8 ExamplesarXiv:2209.11755Zhuyun Dai, Vincent Y. Zhao, Ji Ma, et al.2022 · 170 citations
  61. AgentTuning: Enabling Generalized Agent Abilities for LLMsarXiv:2310.12823Aohan Zeng, Mingdao Liu, Rui Lu, et al.2023 · 163 citations
  62. Orca 2: Teaching Small Language Models How to ReasonarXiv:2311.11045Arindam Mitra, Luciano Del Corro, Shweti Mahajan, et al.2023 · 162 citations
  63. 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
  64. SFT Memorizes, RL Generalizes: A Comparative Study of Foundation Model Post-trainingarXiv:2501.17161Tianzhe Chu, Yuexiang Zhai, Jihan Yang, et al.2025 · 160 citations
  65. A Comprehensive Survey of Small Language Models in the Era of Large Language Models: Techniques, Enhancements, Applications, Collaboration with LLMs, and TrustworthinessACM Transactions on Intelligent Systems and TechnologyFali Wang, Zhiwei Zhang, Xianren Zhang, et al.2024 · 159 citations
  66. MindMap: Knowledge Graph Prompting Sparks Graph of Thoughts in Large Language ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Yilin Wen, Zifeng Wang, Jimeng Sun2024 · 158 citations
  67. MegaScale: Scaling Large Language Model Training to More Than 10,000 GPUsarXiv:2402.15627Ziheng Jiang, Haibin Lin, Yinmin Zhong, et al.2024 · 157 citations
  68. Are aligned neural networks adversarially aligned?Conference on Neural Information Processing Systems (NeurIPS)Nicholas Carlini, Milad Nasr, Christopher A. Choquette-Choo, et al.2023 · 157 citations
  69. ART: Automatic multi-step reasoning and tool-use for large language modelsarXiv:2303.09014Bhargavi Paranjape, Scott Lundberg, Sameer Singh, et al.2023 · 154 citations
  70. The Unlocking Spell on Base LLMs: Rethinking Alignment via In-Context LearningarXiv:2312.01552Bill Yuchen Lin, Abhilasha Ravichander, Ximing Lu, et al.2023 · 150 citations
  71. RA-DIT: Retrieval-Augmented Dual Instruction TuningarXiv:2310.01352Xi Victoria Lin, Xilun Chen, Mingda Chen, et al.2023 · 149 citations
  72. Magpie: Alignment Data Synthesis from Scratch by Prompting Aligned LLMs with NothingarXiv:2406.08464Zhangchen Xu, Fengqing Jiang, Luyao Niu, et al.2024 · 148 citations
  73. Context-faithful Prompting for Large Language ModelsFindings of the Association for Computational LinguisticsWenxuan Zhou, Sheng Zhang, Hoifung Poon, et al.2023 · 148 citations
  74. Fast-DetectGPT: Efficient Zero-Shot Detection of Machine-Generated Text via Conditional Probability CurvaturearXiv:2310.05130Guangsheng Bao, Yanbin Zhao, Zhiyang Teng, et al.2023 · 148 citations
  75. Instruction Induction: From Few Examples to Natural Language Task DescriptionsAnnual Meeting of the Association for Computational Linguistics (ACL)Or Honovich, Uri Shaham, Samuel R. Bowman, et al.2022 · 148 citations
  76. GPT4Tools: Teaching Large Language Model to Use Tools via Self-instructionConference on Neural Information Processing Systems (NeurIPS)Rui Yang, Lin Song, Yanwei Li, et al.2023 · 147 citations
  77. Is GPT-3 a Good Data Annotator?arXiv:2212.10450Bosheng Ding, Chengwei Qin, Linlin Liu, et al.2022 · 147 citations
  78. MiniLLM: On-Policy Distillation of Large Language ModelsarXiv:2306.08543Yuxian Gu, Li Dong, Furu Wei, et al.2023 · 144 citations
  79. A Stitch in Time Saves Nine: Detecting and Mitigating Hallucinations of LLMs by Validating Low-Confidence GenerationarXiv:2307.03987Neeraj Varshney; Wenlin Yao; Hongming Zhang; Jianshu Chen;, Dong Yu2023 · 143 citations
  80. DeepInception: Hypnotize Large Language Model to Be JailbreakerarXiv:2311.03191Xuan Li, Zhanke Zhou, Jianing Zhu, et al.2023 · 141 citations
  81. MINT: Evaluating LLMs in Multi-turn Interaction with Tools and Language FeedbackarXiv:2309.10691Xingyao Wang, Zihan Wang, Jiateng Liu, et al.2023 · 141 citations
  82. 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
  83. 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 · 140 citations
  84. Self-Alignment with Instruction BacktranslationarXiv:2308.06259Xian Li, Ping Yu, Chunting Zhou, et al.2023 · 139 citations
  85. Attributed Question Answering: Evaluation and Modeling for Attributed Large Language ModelsarXiv:2212.08037Bernd Bohnet, Vinh Q. Tran, Pat Verga, et al.2022 · 138 citations
  86. InFoBench: Evaluating Instruction Following Ability in Large Language ModelsFindings of the Association for Computational LinguisticsYiwei Qin, Kaiqiang Song, Yebowen Hu, et al.2024 · 136 citations
  87. A fine-grained comparison of pragmatic language understanding in humans and language modelsJennifer Hu, Sammy Floyd, Olessia Jouravlev, et al.2023 · 136 citations
  88. Simple synthetic data reduces sycophancy in large language modelsarXiv:2308.03958Jerry Wei, Da Huang, Yifeng Lu, et al.2023 · 134 citations
  89. The Impact of Positional Encoding on Length Generalization in TransformersConference on Neural Information Processing Systems (NeurIPS)Amirhossein Kazemnejad, Inkit Padhi, Karthikeyan Natesan Ramamurthy, et al.2023 · 134 citations
  90. FLUTE: Figurative Language Understanding through Textual ExplanationsConference on Empirical Methods in Natural Language Processing (EMNLP)Tuhin Chakrabarty, Arkadiy Saakyan, Debanjan Ghosh, et al.2022 · 133 citations
  91. Task-aware Retrieval with InstructionsFindings of the Association for Computational LinguisticsAkari Asai, Timo Schick, Patrick Lewis, et al.2023 · 132 citations
  92. ZeroSCROLLS: A Zero-Shot Benchmark for Long Text UnderstandingFindings of the Association for Computational LinguisticsUri Shaham, Maor Ivgi, Avia Efrat, et al.2023 · 132 citations
  93. Olmo 3arXiv:2512.13961Team Olmo: Allyson Ettinger, Amanda Bertsch, Bailey Kuehl, et al.2025 · 130 citations
  94. A Survey on Recent Advances in LLM-Based Multi-turn Dialogue SystemsACM Computing SurveysZihao Yi, Jiarui Ouyang, Zhe Xu, et al.2024 · 130 citations
  95. Retrieval meets Long Context Large Language ModelsarXiv:2310.03025Peng Xu, Wei Ping, Xianchao Wu, et al.2023 · 130 citations
  96. Time Travel in LLMs: Tracing Data Contamination in Large Language ModelsarXiv:2308.08493Shahriar Golchin, Mihai Surdeanu2023 · 128 citations
  97. Chain of Hindsight Aligns Language Models with FeedbackarXiv:2302.02676Hao Liu, Carmelo Sferrazza, Pieter Abbeel2023 · 126 citations
  98. LEVER: Learning to Verify Language-to-Code Generation with ExecutionarXiv:2302.08468Ansong Ni, Srini Iyer, Dragomir Radev, et al.2023 · 126 citations
  99. Take a Step Back: Evoking Reasoning via Abstraction in Large Language ModelsarXiv:2310.06117Huaixiu Steven Zheng, Swaroop Mishra, Xinyun Chen, et al.2023 · 126 citations
  100. PEER: A Collaborative Language ModelarXiv:2208.11663Timo Schick, Jane Dwivedi-Yu, Zhengbao Jiang, et al.2022 · 126 citations

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