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Samuel R. Bowman — all works

121 works · page 1 of 2 · 1–100 · ← Samuel R. Bowman

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  1. GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language UnderstandingEMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLPAlex Wang, Amanpreet Singh, Julian Michael, et al.2018 · 7,276 citations
  2. A Broad-Coverage Challenge Corpus for Sentence Understanding through InferenceConference of the North American Chapter of the Association for Computational Linguistics (NAACL)Adina Williams, Nikita Nangia, Samuel R. Bowman2018 · 5,551 citations
  3. A large annotated corpus for learning natural language inferenceConference on Empirical Methods in Natural Language Processing (EMNLP)Samuel R. Bowman, Gabor Angeli, Christopher Potts, et al.2015 · 5,299 citations
  4. Generating Sentences from a Continuous SpaceConference on Computational Natural Language Learning (CoNLL)Samuel R. Bowman, Luke Vilnis, Oriol Vinyals, et al.2016 · 2,435 citations
  5. SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding SystemsarXiv:1905.00537Alex Wang, Yada Pruksachatkun, Nikita Nangia, et al.2019 · 2,227 citations
  6. XNLI: Evaluating Cross-lingual Sentence Representations ↗Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing · no summary yetAlexis Conneau et al.2018 · 1,885 citations
  7. Neural Network Acceptability Judgments ↗Transactions of the ACL 7: 625–641 (2019) · no summary yetAlex Warstadt, Amanpreet Singh, Samuel R. Bowman2019 · 1,765 citations
  8. Annotation Artifacts in Natural Language Inference DataConference of the North American Chapter of the Association for Computational Linguistics (NAACL)Suchin Gururangan, Swabha Swayamdipta, Omer Levy, et al.2018 · 1,615 citations
  9. GPQA: A Graduate-Level Google-Proof Q&A BenchmarkarXiv:2311.12022David Rein, Betty Li Hou, Asa Cooper Stickland, et al.2023 · 1,018 citations
  10. CrowS-Pairs: A Challenge Dataset for Measuring Social Biases in Masked Language ModelsConference on Empirical Methods in Natural Language Processing (EMNLP)Nikita Nangia, Clara Vania, Rasika Bhalerao, et al.2020 · 897 citations
  11. Language Models (Mostly) Know What They Know ↗arXiv:2207.05221 · no summary yetSaurav Kadavath et al.2022 · 827 citations
  12. On Measuring Social Biases in Sentence EncodersConference of the North American Chapter of the Association for Computational Linguistics (NAACL)Chandler May, Alex Wang, Shikha Bordia, et al.2019 · 769 citations
  13. What do you learn from context? Probing for sentence structure in contextualized word representationsarXiv:1905.06316Ian Tenney, Patrick Xia, Berlin Chen, et al.2019 · 602 citations
  14. Language Models Don't Always Say What They Think: Unfaithful Explanations in Chain-of-Thought PromptingConference on Neural Information Processing Systems (NeurIPS)Miles Turpin, Julian Michael, Ethan Perez, et al.2023 · 586 citations
  15. Sentence Encoders on STILTs: Supplementary Training on Intermediate Labeled-data TasksarXiv:1811.01088Jason Phang, Thibault Févry, Samuel R. Bowman2018 · 503 citations
  16. 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
  17. Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons LearnedarXiv:2209.07858Deep Ganguli, Liane Lovitt, Jackson Kernion, et al.2022 · 485 citations
  18. A Gold Standard Dependency Corpus for English ↗Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14) · no summary yetNatalia Silveira et al.2014 · 418 citations
  19. Towards Understanding Sycophancy in Language ModelsarXiv:2310.13548Mrinank Sharma, Meg Tong, Tomasz Korbak, et al.2023 · 384 citations
  20. Identifying and Reducing Gender Bias in Word-Level Language ModelsarXiv:1904.03035Shikha Bordia, Samuel R. Bowman2019 · 367 citations
  21. A Fast Unified Model for Parsing and Sentence Understanding ↗Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · no summary yetSamuel R. Bowman et al.2016 · 323 citations
  22. 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
  23. LLM Evaluators Recognize and Favor Their Own Generations ↗arXiv:2404.13076 · no summary yetArjun Panickssery, Samuel R. Bowman, Shi Feng2024 · 212 citations
  24. What Will it Take to Fix Benchmarking in Natural Language Understanding? ↗Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · no summary yetSamuel R. Bowman, George Dahl2021 · 204 citations
  25. Learning Which Features Matter: RoBERTa Acquires a Preference for Linguistic Generalizations (Eventually) ↗Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · no summary yetAlex Warstadt et al.2020 · 187 citations
  26. Language Modeling Teaches You More than Translation Does: Lessons Learned Through Auxiliary Syntactic Task Analysis ↗Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP · no summary yetKelly Zhang, Samuel R. Bowman2018 · 178 citations
  27. What Artificial Neural Networks Can Tell Us About Human Language AcquisitionarXiv:2208.07998Alex Warstadt, Samuel R. Bowman2022 · 177 citations
  28. When Do You Need Billions of Words of Pretraining Data? ↗Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) · no summary yetYian Zhang et al.2021 · 176 citations
  29. Do Attention Heads in BERT Track Syntactic Dependencies? ↗arXiv (Cornell University) · no summary yetPhu Mon Htut et al.2019 · 175 citations
  30. QuALITY: Question Answering with Long Input Texts, Yes!Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)Richard Yuanzhe Pang, Alicia Parrish, Nitish Joshi, et al.2022 · 167 citations
  31. Recursive Neural Networks Can Learn Logical Semantics ↗Proceedings of the 3rd Workshop on Continuous Vector Space Models and their Compositionality · no summary yetSamuel R. Bowman, Christopher Potts, Christopher D. Manning2015 · 167 citations
  32. Probing What Different NLP Tasks Teach Machines about Function Word ComprehensionJoint Conference on Lexical and Computational Semantics (*SEM)Najoung Kim, Roma Patel, Adam Poliak, et al.2019 · 163 citations
  33. Can You Tell Me How to Get Past Sesame Street? Sentence-Level Pretraining Beyond Language Modeling ↗Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · no summary yetAlex Wang et al.2019 · 154 citations
  34. Do latent tree learning models identify meaningful structure in sentences?Transactions of the Association for Computational Linguistics (TACL)Adina Williams, Andrew Drozdov, Samuel R. Bowman2018 · 149 citations
  35. Eight Things to Know about Large Language Models ↗Critical AI · no summary yetSamuel R. Bowman2024 · 148 citations
  36. Instruction Induction: From Few Examples to Natural Language Task Descriptions ↗arXiv:2205.10782 · no summary yetOr Honovich et al.2022 · 148 citations
  37. Discourse-Based Objectives for Fast Unsupervised Sentence Representation Learning ↗arXiv (Cornell University) · no summary yetYacine Jernite, Samuel R. Bowman, David A. Sontag2017 · 142 citations
  38. The RepEval 2017 Shared Task: Multi-Genre Natural Language Inference with Sentence RepresentationsWorkshop on Evaluating Vector Space Representations for NLPNikita Nangia, Adina Williams, Angeliki Lazaridou, et al.2017 · 128 citations
  39. Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training ↗arXiv:2401.05566 · no summary yetEvan Hubinger et al.2024 · 123 citations
  40. Pretraining Language Models with Human PreferencesarXiv:2302.08582Tomasz Korbak, Kejian Shi, Angelica Chen, et al.2023 · 121 citations
  41. Measuring Progress on Scalable Oversight for Large Language ModelsarXiv:2211.03540Samuel R. Bowman, Jeeyoon Hyun, Ethan Perez, et al.2022 · 117 citations
  42. Idiosyncratically Transparent Vowels in Kazakh ↗Proceedings of the Annual Meetings on Phonology · no summary yetSamuel R. Bowman, Benjamin Lokshin2014 · 109 citations
  43. Reasoning Models Don't Always Say What They Think ↗arXiv:2505.05410 · no summary yetYanda Chen et al.2025 · 106 citations
  44. Studying Large Language Model Generalization with Influence Functions ↗arXiv:2308.03296 · no summary yetRoger Grosse et al.2023 · 105 citations
  45. Debating with More Persuasive LLMs Leads to More Truthful Answers ↗arXiv:2402.06782 · no summary yetAkbir Khan et al.2024 · 101 citations
  46. ListOps: A Diagnostic Dataset for Latent Tree Learning ↗Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop · no summary yetNikita Nangia, Samuel R. Bowman2018 · 96 citations
  47. jiant: A Software Toolkit for Research on General-Purpose Text Understanding Models ↗Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations · no summary yetYada Pruksachatkun et al.2020 · 89 citations
  48. Human vs. Muppet: A Conservative Estimate of Human Performance on the GLUE Benchmark ↗Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · no summary yetNikita Nangia, Samuel R. Bowman2019 · 87 citations
  49. Intermediate-Task Transfer Learning with Pretrained Models for Natural Language Understanding: When and Why Does It Work? ↗arXiv (Cornell University) · no summary yetYada Pruksachatkun et al.2020 · 83 citations
  50. Alignment faking in large language models ↗arXiv (Cornell University) · no summary yetRyan Greenblatt et al.2024 · 82 citations
  51. Question Decomposition Improves the Faithfulness of Model-Generated Reasoning ↗arXiv (Cornell University) · no summary yetAnsh Radhakrishnan et al.2023 · 74 citations
  52. Tree-structured composition in neural networks without tree-structured architectures ↗arXiv:1506.04834 · no summary yetSamuel R. Bowman, Christopher D. Manning, Christopher Potts2015 · 73 citations
  53. The Dangers of Underclaiming: Reasons for Caution When Reporting How NLP Systems Fail ↗Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · no summary yetBowman, Samuel R.2022 · 72 citations
  54. English Intermediate-Task Training Improves Zero-Shot Cross-Lingual Transfer TooConference of the Asia-Pacific Chapter of the Association for Computational Linguistics (AACL)Jason Phang, Iacer Calixto, Phu Mon Htut, et al.2020 · 70 citations
  55. Can neural networks acquire a structural bias from raw linguistic data? ↗arXiv (Cornell University) · no summary yetAlex Warstadt, Samuel R. Bowman2020 · 70 citations
  56. Verb Argument Structure Alternations in Word and Sentence Embeddings ↗Proceedings of the Society for Computation in Linguistics (SCiL) 2019 · no summary yetKatharina Kann et al.2019 · 68 citations
  57. Let's Think Dot by Dot: Hidden Computation in Transformer Language Models ↗arXiv:2404.15758 · no summary yetJacob Pfau, William Merrill, Samuel R. Bowman2024 · 67 citations
  58. Language Modeling Teaches You More Syntax than Translation Does: Lessons Learned Through Auxiliary Task Analysis ↗arXiv:1809.10040 · Blackbox NLP Workshop, EMNLP 2018 · no summary yetKelly W. Zhang, Samuel R. Bowman2018 · 65 citations
  59. Towards Realistic Practices In Low-Resource Natural Language Processing: The Development Set ↗Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) · no summary yetKatharina Kann, Kyunghyun Cho, Samuel R. Bowman2019 · 62 citations
  60. Grammar Induction with Neural Language Models: An Unusual Replication ↗Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing · no summary yetPhu Mon Htut, Kyunghyun Cho, Samuel R. Bowman2018 · 61 citations
  61. Training a Ranking Function for Open-Domain Question Answering ↗Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop · no summary yetPhu Mon Htut, Samuel R. Bowman, Kyunghyun Cho2018 · 54 citations
  62. SQuALITY: Building a Long-Document Summarization Dataset the Hard Way ↗Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing · no summary yetAlex Wang et al.2022 · 51 citations
  63. Counterfactually-Augmented SNLI Training Data Does Not Yield Better Generalization Than Unaugmented DataWorkshop on Insights from Negative Results in NLPWilliam Huang, Haokun Liu, Samuel R. Bowman2020 · 51 citations
  64. Ruminating Reader: Reasoning with Gated Multi-hop Attention ↗Proceedings of the Workshop on Machine Reading for Question Answering · no summary yetYichen Gong, Samuel R. Bowman2018 · 50 citations
  65. Does Putting a Linguist in the Loop Improve NLU Data Collection? ↗Findings of the Association for Computational Linguistics: EMNLP 2021 · no summary yetAlicia Parrish et al.2021 · 49 citations
  66. What Ingredients Make for an Effective Crowdsourcing Protocol for Difficult NLU Data Collection Tasks? ↗Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) · no summary yetNikita Nangia et al.2021 · 48 citations
  67. Improving Code Generation by Training with Natural Language Feedback ↗arXiv:2303.16749 · no summary yetAngelica Chen et al.2023 · 47 citations
  68. Comparing Test Sets with Item Response Theory ↗Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) · no summary yetClara Vania et al.2021 · 47 citations
  69. More Constructions, More Genres: Extending Stanford DependenciesMarie-Catherine de Marneffe, Miriam Connor, Natalia Silveira, et al.English · 47 citations
  70. Sycophancy to Subterfuge: Investigating Reward-Tampering in Large Language Models ↗arXiv (Cornell University) · no summary yetCarson Denison et al.2024 · 46 citations
  71. Grammatical Analysis of Pretrained Sentence Encoders with Acceptability Judgments. ↗arXiv (Cornell University) · no summary yetAlex Warstadt, Samuel R. Bowman2019 · 45 citations
  72. Detecting and Explaining Crisis ↗Proceedings of the Fourth Workshop on Computational Linguistics and Clinical Psychology –- From Linguistic Signal to Clinical Reality · no summary yetRohan Kshirsagar, Robert Morris, Samuel R. Bowman2017 · 41 citations
  73. Can recursive neural tensor networks learn logical reasoning? ↗arXiv (Cornell University) · no summary yetSamuel R. Bowman2013 · 41 citations
  74. What Do NLP Researchers Believe? Results of the NLP Community Metasurvey ↗Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · no summary yetJulian Michael et al.2023 · 40 citations
  75. NOPE: A Corpus of Naturally-Occurring Presuppositions in EnglishAlicia Parrish, Sebastian Schuster, Alex Warstadt, et al.English · 40 citations
  76. A Stable and Effective Learning Strategy for Trainable Greedy Decoding ↗Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing · no summary yetYun Chen et al.2018 · 40 citations
  77. (QA)²: Question Answering with Questionable Assumptions ↗Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · no summary yetNajoung Kim et al.2023 · 39 citations
  78. Fine-Tuned Transformers Show Clusters of Similar Representations Across Layers ↗Proceedings of the Fourth BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP · no summary yetJason Phang, Haokun Liu, Samuel R. Bowman2021 · 39 citations
  79. Speech recognitionwith segmental conditional random fields: A summary of the JHU CLSP 2010 Summer Workshop ↗IEEE International Conference on Acoustics Speech and Signal Processing · no summary yetGeoffrey Zweig et al.2011 · 37 citations
  80. Looking for ELMo's friends: Sentence-Level Pretraining Beyond Language Modeling. ↗no summary yetSamuel R. Bowman et al.2018 · 34 citations
  81. Language Models Learn to Mislead Humans via RLHF ↗arXiv (Cornell University) · no summary yetJiaxin Wen et al.2024 · 33 citations
  82. Can Unconditional Language Models Recover Arbitrary Sentences? ↗arXiv (Cornell University) · no summary yetNishant Subramani, Samuel R. Bowman, Kyunghyun Cho2019 · 29 citations
  83. Debate Helps Supervise Unreliable Experts ↗arXiv (Cornell University) · no summary yetJulian Michael et al.2023 · 28 citations
  84. Deep Learning for Natural Language Inference ↗Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Tutorials · no summary yetSamuel R. Bowman, Xiaodan Zhu2019 · 28 citations
  85. ScoNe: Benchmarking Negation Reasoning in Language Models With Fine-Tuning and In-Context Learning ↗Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) · no summary yetJingyuan S. She et al.2023 · 27 citations
  86. New Protocols and Negative Results for Textual Entailment Data CollectionConference on Empirical Methods in Natural Language Processing (EMNLP)Samuel R. Bowman, Jennimaria Palomaki, Livio Baldini Soares, et al.2020 · 27 citations
  87. Automatic Animacy Classification ↗Proceedings of the NAACL HLT 2012 Student Research Workshop · no summary yetSamuel R. Bowman, Harshit Chopra2012 · 26 citations
  88. Two Failures of Self-Consistency in the Multi-Step Reasoning of LLMsarXiv:2305.14279 · Transactions on Machine Learning Research (2024)Angelica Chen, Jason Phang, Alicia Parrish, et al.2023 · 25 citations
  89. SocioProbe: What, When, and Where Language Models Learn about Sociodemographics ↗Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing · no summary yetAnne Lauscher et al.2022 · 24 citations
  90. What Makes Reading Comprehension Questions Difficult? ↗Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · no summary yetSaku Sugawara et al.2022 · 21 citations
  91. BLiMP: The Benchmark of Linguistic Minimal Pairs for EnglishAlex Warstadt, Alicia Parrish, Haokun Liu, et al.Mova's core list · 21 citations
  92. Auditing language models for hidden objectives ↗arXiv (Cornell University) · no summary yetMarks, Samuel et al.2025 · 19 citations
  93. Adversarially Constructed Evaluation Sets Are More Challenging, but May Not Be Fair ↗Proceedings of the First Workshop on Dynamic Adversarial Data Collection · no summary yetJason Phang et al.2022 · 18 citations
  94. Precise Task Formalization Matters in Winograd Schema Evaluations ↗Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · no summary yetHaokun Liu et al.2020 · 18 citations
  95. Steering Without Side Effects: Improving Post-Deployment Control of Language Models ↗arXiv (Cornell University) · no summary yetAsa Cooper Stickland et al.2024 · 17 citations
  96. Learning Distributed Word Representations for Natural Logic Reasoning ↗arXiv (Cornell University) · no summary yetSamuel R. Bowman, Christopher E. Potts, Christopher D. Manning2014 · 17 citations
  97. A Molecular Toolkit to Visualize Native Protein Assemblies in the Context of Human Disease ↗Scientific Reports · no summary yetBrian L. Gilmore et al.2015 · 16 citations
  98. Learning to Learn Morphological Inflection for Resource-Poor Languages ↗Proceedings of the AAAI Conference on Artificial Intelligence · no summary yetKatharina Kann, Samuel R. Bowman, Kyunghyun Cho2020 · 14 citations
  99. Single-Turn Debate Does Not Help Humans Answer Hard Reading-Comprehension Questions ↗Proceedings of the First Workshop on Learning with Natural Language Supervision · no summary yetAlicia Parrish et al.2022 · 13 citations
  100. Sequential Attention: A Context-Aware Alignment Function for Machine Reading ↗Proceedings of the 2nd Workshop on Representation Learning for NLP · no summary yetSebastian Brarda, Philip Yeres, Samuel R. Bowman2017 · 11 citations

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