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Who cites Iason Gabriel

1,461 citations of their works from 1,278 papers of our library (of the 149,439 whose bibliographies we read) · 405 with a page here, the most cited first · page 3 of 5 · 201–300 · ← Iason Gabriel

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  1. Progressive-Hint Prompting Improves Reasoning in Large Language ModelsarXiv:2304.09797Chuanyang Zheng, Zhengying Liu, Enze Xie, et al.2023 · 131 citations
  2. A Survey on Recent Advances in LLM-Based Multi-turn Dialogue SystemsACM Computing SurveysZihao Yi, Jiarui Ouyang, Zhe Xu, et al.2024 · 130 citations
  3. OpenCodeInterpreter: Integrating Code Generation with Execution and RefinementFindings of the Association for Computational LinguisticsTianyu Zheng, Ge Zhang, Tianhao Shen, et al.2024 · 129 citations
  4. Evaluating the Moral Beliefs Encoded in LLMsConference on Neural Information Processing Systems (NeurIPS)Nino Scherrer, Claudia Shi, Amir Feder, et al.2023 · 129 citations
  5. SafeDecoding: Defending against Jailbreak Attacks via Safety-Aware DecodingAnnual Meeting of the Association for Computational Linguistics (ACL)Zhangchen Xu, Fengqing Jiang, Luyao Niu, et al.2024 · 127 citations
  6. The WMDP Benchmark: Measuring and Reducing Malicious Use With UnlearningarXiv:2403.03218Nathaniel Li, Alexander Pan, Anjali Gopal, et al.2024 · 127 citations
  7. NaijaSenti: A Nigerian Twitter Sentiment Corpus for Multilingual Sentiment AnalysisLanguage Resources and Evaluation Conference (LREC)Shamsuddeen Hassan Muhammad, David Ifeoluwa Adelani, Sebastian Ruder, et al.2022 · 127 citations
  8. PEER: A Collaborative Language ModelarXiv:2208.11663Timo Schick, Jane Dwivedi-Yu, Zhengbao Jiang, et al.2022 · 126 citations
  9. Large Language Model Alignment: A SurveyarXiv:2309.15025Tianhao Shen, Renren Jin, Yufei Huang, et al.2023 · 124 citations
  10. Unlearn What You Want to Forget: Efficient Unlearning for LLMsConference on Empirical Methods in Natural Language Processing (EMNLP)Jiaao Chen, Diyi Yang2023 · 124 citations
  11. Emergent and Predictable Memorization in Large Language ModelsConference on Neural Information Processing Systems (NeurIPS)Stella Biderman, USVSN Sai Prashanth, Lintang Sutawika, et al.2023 · 123 citations
  12. Faithful Reasoning Using Large Language ModelsarXiv:2208.14271Antonia Creswell, Murray Shanahan2022 · 122 citations
  13. The Alignment Problem from a Deep Learning PerspectivearXiv:2209.00626Richard Ngo, Lawrence Chan, Soren Mindermann2022 · 121 citations
  14. Best Practices and Lessons Learned on Synthetic DataarXiv:2404.07503Ruibo Liu, Jerry Wei, Fangyu Liu, et al.2024 · 119 citations
  15. LLM Self Defense: By Self Examination, LLMs Know They Are Being TrickedarXiv:2308.07308Mansi Phute, Alec Helbling, Matthew Hull, et al.2023 · 119 citations
  16. Value Kaleidoscope: Engaging AI with Pluralistic Human Values, Rights, and DutiesProceedings of the AAAI Conference on Artificial IntelligenceTaylor Sorensen, Liwei Jiang, Jena Hwang, et al.2023 · 116 citations
  17. Fundamental Limitations of Alignment in Large Language ModelsarXiv:2304.11082Yotam Wolf, Noam Wies, Oshri Avnery, et al.2023 · 114 citations
  18. Ground-Truth Labels Matter: A Deeper Look into Input-Label DemonstrationsConference on Empirical Methods in Natural Language Processing (EMNLP)Kang Min Yoo, Junyeob Kim, Hyuhng Joon Kim, et al.2022 · 114 citations
  19. ALLaM: Large Language Models for Arabic and EnglisharXiv:2407.15390M Saiful Bari, Yazeed Alnumay, Norah A. Alzahrani, et al.2024 · 113 citations
  20. Certifying LLM Safety against Adversarial PromptingarXiv:2309.02705Aounon Kumar, Chirag Agarwal, Suraj Srinivas, et al.2023 · 113 citations
  21. Efficient Large Scale Language Modeling with Mixtures of ExpertsarXiv:2112.10684Mikel Artetxe, Shruti Bhosale, Naman Goyal, et al.2021 · 113 citations
  22. Efficient Large Language Models: A SurveyarXiv:2312.03863Zhongwei Wan, Xin Wang, Che Liu, et al.2023 · 110 citations
  23. Can Machines Learn Morality? The Delphi ExperimentarXiv:2110.07574Liwei Jiang, Jena D. Hwang, Chandra Bhagavatula, et al.2021 · 110 citations
  24. Better Zero-Shot Reasoning with Role-Play PromptingarXiv:2308.07702Aobo Kong, Shiwan Zhao, Hao Chen, et al.2023 · 109 citations
  25. CodeIE: Large Code Generation Models are Better Few-Shot Information ExtractorsAnnual Meeting of the Association for Computational Linguistics (ACL)Peng Li, Tianxiang Sun, Qiong Tang, et al.2023 · 109 citations
  26. SemDeDup: Data-efficient learning at web-scale through semantic deduplicationarXiv:2303.09540Amro Abbas, Kushal Tirumala, Dániel Simig, et al.2023 · 108 citations
  27. Multi-Step Reasoning with Large Language Models, a SurveyACM Computing SurveysAske Plaat, Annie Wong, Suzan Verberne, et al.2024 · 106 citations
  28. Inverse Scaling: When Bigger Isn't BetterarXiv:2306.09479 · Transactions on Machine Learning Research (TMLR), 10/2023, https://openreview.net/forum?id=DwgRm72GQFIan R. McKenzie, Alexander Lyzhov, Michael Pieler, et al.2023 · 105 citations
  29. Studying Large Language Model Generalization with Influence FunctionsarXiv:2308.03296Roger Grosse, Juhan Bae, Cem Anil, et al.2023 · 105 citations
  30. Rich Knowledge Sources Bring Complex Knowledge Conflicts: Recalibrating Models to Reflect Conflicting EvidenceConference on Empirical Methods in Natural Language Processing (EMNLP)Hung-Ting Chen, Michael Zhang, Eunsol Choi2022 · 105 citations
  31. A Pretrainer's Guide to Training Data: Measuring the Effects of Data Age, Domain Coverage, Quality, & ToxicityarXiv:2305.13169Shayne Longpre, Gregory Yauney, Emily Reif, et al.2023 · 104 citations
  32. Model evaluation for extreme risksarXiv:2305.15324Toby Shevlane, Sebastian Farquhar, Ben Garfinkel, et al.2023 · 104 citations
  33. Towards Measuring and Modeling “Culture” in LLMs: A SurveyConference on Empirical Methods in Natural Language Processing (EMNLP)Muhammad Farid Adilazuarda, Sagnik Mukherjee, Pradhyumna Lavania, et al.2024 · 103 citations
  34. PolyLM: An Open Source Polyglot Large Language ModelarXiv:2307.06018Xiangpeng Wei, Haoran Wei, Huan Lin, et al.2023 · 103 citations
  35. LUT-GEMM: Quantized Matrix Multiplication based on LUTs for Efficient Inference in Large-Scale Generative Language ModelsInternational Conference on Learning Representations (ICLR)Gunho Park, Baeseong Park, Minsub Kim, et al.2022 · 103 citations
  36. Investigating the Factual Knowledge Boundary of Large Language Models with Retrieval AugmentationInternational Conference on Computational Linguistics (COLING)Ruiyang Ren, Yuhao Wang, Yingqi Qu, et al.2025 · 101 citations
  37. Large language models for education: A survey and outlookWang2024 · 101 citations
  38. FLASK: Fine-grained Language Model Evaluation based on Alignment Skill SetsarXiv:2307.10928Seonghyeon Ye, Doyoung Kim, Sungdong Kim, et al.2023 · 100 citations
  39. FineWeb2: One Pipeline to Scale Them All — Adapting Pre-Training Data Processing to Every LanguagearXiv:2506.20920Guilherme Penedo, Hynek Kydliček, Vinko Sabolčec, et al.2025 · 99 citations
  40. Provable Robust Watermarking for AI-Generated TextarXiv:2306.17439Xuandong Zhao, Prabhanjan Ananth, Lei Li, et al.2023 · 98 citations
  41. Sparse Upcycling: Training Mixture-of-Experts from Dense CheckpointsarXiv:2212.05055Aran Komatsuzaki, Joan Puigcerver, James Lee-Thorp, et al.2022 · 98 citations
  42. Safety Alignment Should Be Made More Than Just a Few Tokens DeeparXiv:2406.05946Xiangyu Qi, Ashwinee Panda, Kaifeng Lyu, et al.2024 · 97 citations
  43. A Survey on Data Selection for Language ModelsarXiv:2402.16827Alon Albalak, Yanai Elazar, Sang Michael Xie, et al.2024 · 95 citations
  44. NLPositionality: Characterizing Design Biases of Datasets and ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Sebastin Santy, Jenny Liang, Ronan Le Bras, et al.2023 · 95 citations
  45. Boosting Language Models Reasoning with Chain-of-Knowledge PromptingAnnual Meeting of the Association for Computational Linguistics (ACL)Jianing Wang, Qiushi Sun, Xiang Li, et al.2024 · 94 citations
  46. Training Socially Aligned Language Models on Simulated Social InteractionsarXiv:2305.16960Ruibo Liu, Ruixin Yang, Chenyan Jia, et al.2023 · 94 citations
  47. Do-Not-Answer: Evaluating Safeguards in LLMsFindings of the Association for Computational LinguisticsYuxia Wang, Haonan Li, Xudong Han, et al.2024 · 93 citations
  48. LLMs Are Few-Shot In-Context Low-Resource Language LearnersConference of the North American Chapter of the Association for Computational Linguistics (NAACL)Samuel Cahyawijaya, Holy Lovenia, Pascale Fung2024 · 93 citations
  49. A Survey of Reinforcement Learning from Human FeedbackTransactions on Machine Learning ResearchTimo Kaufmann, Paul Weng, Viktor Bengs, et al.2023 · 92 citations
  50. On the Tool Manipulation Capability of Open-source Large Language ModelsarXiv:2305.16504Qiantong Xu, Fenglu Hong, Bo Li, et al.2023 · 92 citations
  51. Language Modeling Is CompressionarXiv:2309.10668Gregoire Deletang, Anian Ruoss, Paul-Ambroise Duquenne, et al.2023 · 91 citations
  52. Rephrasing the Web: A Recipe for Compute and Data-Efficient Language ModelingAnnual Meeting of the Association for Computational Linguistics (ACL)Pratyush Maini, Skyler Seto, Richard Bai, et al.2024 · 90 citations
  53. Alignment for HonestyConference on Neural Information Processing Systems (NeurIPS)Yuqing Yang, Ethan Chern, Xipeng Qiu, et al.2023 · 90 citations
  54. Language Generation Models Can Cause Harm: So What Can We Do About It? An Actionable SurveyConference of the European Chapter of the Association for Computational Linguistics (EACL)Sachin Kumar, Vidhisha Balachandran, Lucille Njoo, et al.2023 · 90 citations
  55. Rewarded soups: towards Pareto-optimal alignment by interpolating weights fine-tuned on diverse rewardsConference on Neural Information Processing Systems (NeurIPS)Alexandre Rame, Guillaume Couairon, Mustafa Shukor, et al.2023 · 90 citations
  56. Automatically Auditing Large Language Models via Discrete OptimizationarXiv:2303.04381Erik Jones, Anca Dragan, Aditi Raghunathan, et al.2023 · 89 citations
  57. LAMBADA: Backward Chaining for Automated Reasoning in Natural LanguageAnnual Meeting of the Association for Computational Linguistics (ACL)Mehran Kazemi, Najoung Kim, Deepti Bhatia, et al.2023 · 89 citations
  58. Foundational Challenges in Assuring Alignment and Safety of Large Language ModelsarXiv:2404.09932Usman Anwar, Abulhair Saparov, Javier Rando, et al.2024 · 87 citations
  59. A Survey of Large Language Models in Medicine: Progress, Application, and ChallengearXiv:2311.05112Hongjian Zhou, Fenglin Liu, Boyang Gu, et al.2023 · 87 citations
  60. Scaling Laws vs Model Architectures: How does Inductive Bias Influence Scaling?Findings of the Association for Computational LinguisticsYi Tay, Mostafa Dehghani, Samira Abnar, et al.2023 · 86 citations
  61. The Goldilocks of Pragmatic Understanding: Fine-Tuning Strategy Matters for Implicature Resolution by LLMsConference on Neural Information Processing Systems (NeurIPS)Laura Ruis, Akbir Khan, Stella Biderman, et al.2022 · 86 citations
  62. On Prompt-Driven Safeguarding for Large Language ModelsarXiv:2401.18018Chujie Zheng, Fan Yin, Hao Zhou, et al.2024 · 85 citations
  63. Bridging the Gap: A Survey on Integrating (Human) Feedback for Natural Language GenerationTransactions of the Association for Computational Linguistics (TACL)Patrick Fernandes, Aman Madaan, Emmy Liu, et al.2023 · 84 citations
  64. AlexaTM 20B: Few-Shot Learning Using a Large-Scale Multilingual Seq2Seq ModelarXiv:2208.01448Saleh Soltan, Shankar Ananthakrishnan, Jack FitzGerald, et al.2022 · 84 citations
  65. ArtPrompt: ASCII Art-based Jailbreak Attacks against Aligned LLMsarXiv:2402.11753Fengqing Jiang, Zhangchen Xu, Luyao Niu, et al.2024 · 83 citations
  66. Survey of Vulnerabilities in Large Language Models Revealed by Adversarial AttacksarXiv:2310.10844Erfan Shayegani, Md Abdullah Al Mamun, Yu Fu, et al.2023 · 83 citations
  67. Do-Not-Answer: A Dataset for Evaluating Safeguards in LLMsarXiv:2308.13387Yuxia Wang, Haonan Li, Xudong Han, et al.2023 · 78 citations
  68. SOLAR 10.7B: Scaling Large Language Models with Simple yet Effective Depth Up-ScalingarXiv:2312.15166Dahyun Kim, Chanjun Park, Sanghoon Kim, et al.2023 · 77 citations
  69. Measuring Political Bias in Large Language Models: What Is Said and How It Is SaidAnnual Meeting of the Association for Computational Linguistics (ACL)Yejin Bang, Delong Chen, Nayeon Lee, et al.2024 · 76 citations
  70. QuRating: Selecting High-Quality Data for Training Language ModelsarXiv:2402.09739Alexander Wettig, Aatmik Gupta, Saumya Malik, et al.2024 · 74 citations
  71. AlignBench: Benchmarking Chinese Alignment of Large Language ModelsarXiv:2311.18743Xiao Liu, Xuanyu Lei, Shengyuan Wang, et al.2023 · 74 citations
  72. What Language Model to Train if You Have One Million GPU Hours?arXiv:2210.15424Teven Le Scao, Thomas Wang, Daniel Hesslow, et al.2022 · 73 citations
  73. Secrets of RLHF in Large Language Models Part II: Reward ModelingarXiv:2401.06080Binghai Wang, Rui Zheng, Lu Chen, et al.2024 · 72 citations
  74. Chain-of-Thought Hub: A Continuous Effort to Measure Large Language Models' Reasoning PerformancearXiv:2305.17306Yao Fu, Litu Ou, Mingyu Chen, et al.2023 · 72 citations
  75. Few-Shot Semantic Parsing with Language Models Trained on CodeConference of the North American Chapter of the Association for Computational Linguistics (NAACL)Richard Shin, Benjamin Van Durme2022 · 72 citations
  76. InPars: Data Augmentation for Information Retrieval using Large Language ModelsarXiv:2202.05144Luiz Bonifacio, Hugo Abonizio, Marzieh Fadaee, et al.2022 · 72 citations
  77. The Ghost in the Machine has an American accent: value conflict in GPT-3arXiv:2203.07785Rebecca L Johnson, Giada Pistilli, Natalia Menedez-Gonzalez, et al.2022 · 72 citations
  78. Transformer Grammars: Augmenting Transformer Language Models with Syntactic Inductive Biases at ScaleTransactions of the Association for Computational Linguistics (TACL)Laurent Sartran, Samuel Barrett, Adhiguna Kuncoro, et al.2022 · 72 citations
  79. Great, Now Write an Article About That: The Crescendo Multi-Turn LLM Jailbreak AttackarXiv:2404.01833Mark Russinovich, Ahmed Salem, Ronen Eldan2024 · 71 citations
  80. Mixture-of-Experts Meets Instruction Tuning:A Winning Combination for Large Language ModelsarXiv:2305.14705Sheng Shen, Le Hou, Yanqi Zhou, et al.2023 · 70 citations
  81. Self-Alignment for Factuality: Mitigating Hallucinations in LLMs via Self-EvaluationAnnual Meeting of the Association for Computational Linguistics (ACL)Xiaoying Zhang, Baolin Peng, Ye Tian, et al.2024 · 69 citations
  82. Inference-Time Scaling for Generalist Reward ModelingarXiv:2504.02495Zijun Liu, Peiyi Wang, Runxin Xu, et al.2025 · 65 citations
  83. Nemotron-CC: Transforming Common Crawl into a Refined Long-Horizon Pretraining DatasetAnnual Meeting of the Association for Computational Linguistics (ACL)Dan Su, Kezhi Kong, Ying Lin, et al.2025 · 65 citations
  84. A Comprehensive Survey on Long Context Language ModelingarXiv:2503.17407Jiaheng Liu, Dawei Zhu, Zhiqi Bai, et al.2025 · 64 citations
  85. Evaluation of OpenAI o1: Opportunities and Challenges of AGIarXiv:2409.18486Tianyang Zhong, Zhengliang Liu, Yi Pan, et al.2024 · 64 citations
  86. OpenMoE: An Early Effort on Open Mixture-of-Experts Language ModelsarXiv:2402.01739Fuzhao Xue, Zian Zheng, Yao Fu, et al.2024 · 64 citations
  87. Can Language Models Handle Recursively Nested Grammatical Structures? A Case Study on Comparing Models and HumansComputational LinguisticsAndrew Lampinen2024 · 63 citations
  88. ERNIE 3.0 Titan: Exploring Larger-scale Knowledge Enhanced Pre-training for Language Understanding and GenerationarXiv:2112.12731Shuohuan Wang, Yu Sun, Yang Xiang, et al.2021 · 63 citations
  89. Large Language Model Agent: A Survey on Methodology, Applications and ChallengesarXiv:2503.21460Junyu Luo, Weizhi Zhang, Ye Yuan, et al.2025 · 62 citations
  90. Exploring the Impact of Instruction Data Scaling on Large Language Models: An Empirical Study on Real-World Use CasesarXiv:2303.14742Yunjie Ji, Yong Deng, Yan Gong, et al.2023 · 62 citations
  91. SatLM: Satisfiability-Aided Language Models Using Declarative PromptingConference on Neural Information Processing Systems (NeurIPS)Xi Ye, Qiaochu Chen, Isil Dillig, et al.2023 · 62 citations
  92. Teaching Large Language Models to Reason with Reinforcement LearningarXiv:2403.04642Alex Havrilla, Yuqing Du, Sharath Chandra Raparthy, et al.2024 · 61 citations
  93. Instruction Pre-Training: Language Models are Supervised Multitask LearnersConference on Empirical Methods in Natural Language Processing (EMNLP)Daixuan Cheng, Yuxian Gu, Shaohan Huang, et al.2024 · 59 citations
  94. Simple linear attention language models balance the recall-throughput tradeoffarXiv:2402.18668Simran Arora, Sabri Eyuboglu, Michael Zhang, et al.2024 · 59 citations
  95. MLLM-Protector: Ensuring MLLM's Safety without Hurting PerformanceConference on Empirical Methods in Natural Language Processing (EMNLP)Renjie Pi, Tianyang Han, Jianshu Zhang, et al.2024 · 58 citations
  96. Does Circuit Analysis Interpretability Scale? Evidence from Multiple Choice Capabilities in ChinchillaarXiv:2307.09458Tom Lieberum, Matthew Rahtz, Janos Kramar, et al.2023 · 58 citations
  97. Beyond Chinchilla-Optimal: Accounting for Inference in Language Model Scaling LawsarXiv:2401.00448Nikhil Sardana, Jacob Portes, Sasha Doubov, et al.2024 · 57 citations
  98. Dialect prejudice predicts AI decisions about people's character, employability, and criminalityarXiv:2403.00742Valentin Hofmann, Pratyusha Ria Kalluri, Dan Jurafsky, et al.2024 · 57 citations
  99. RegMix: Data Mixture as Regression for Language Model Pre-trainingarXiv:2407.01492Qian Liu, Xiaosen Zheng, Niklas Muennighoff, et al.2024 · 57 citations
  100. Personalisation within bounds: A risk taxonomy and policy framework for the alignment of large language models with personalised feedbackarXiv:2303.05453Hannah Rose Kirk, Bertie Vidgen, Paul Rottger, et al.2023 · 57 citations

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