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Who cites «The CommitmentBank: Investigating projection in naturally occurring discourse»

234 papers of our library cite it (of the 140,884 whose bibliographies we read) · 39 with a page here, the most cited first · ← the paper

  1. Language Models are Few-Shot LearnersTom B. Brown, Benjamin Mann, Nick Ryder, et al.2020 · 10,782 citations
  2. Exploring the Limits of Transfer Learning with a Unified Text-to-Text TransformerColin Raffel, Noam Shazeer, Adam Roberts, et al.2019 · 7,433 citations
  3. SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding SystemsAlex Wang, Yada Pruksachatkun, Nikita Nangia, et al.2019 · 2,187 citations
  4. DeBERTa: Decoding-enhanced BERT with Disentangled AttentionPengcheng He, Xiaodong Liu, Jianfeng Gao, et al.2020 · 1,800 citations
  5. Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language modelsAarohi Srivastava, Abhinav Rastogi, Abhishek Rao, et al.2022 · 1,695 citations
  6. Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?Sewon Min, Xinxi Lyu, Ari Holtzman, et al.2022 · 1,423 citations
  7. A Comprehensive Overview of Large Language ModelsHumza Naveed, Asad Ullah Khan, Shi Qiu, et al.2023 · 1,286 citations
  8. Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order SensitivityYao Lu, Max Bartolo, Alastair Moore, et al.2022 · 1,187 citations
  9. It's Not Just Size That Matters: Small Language Models Are Also Few-Shot LearnersTimo Schick, Hinrich Schütze2021 · 1,118 citations
  10. GPT Understands, TooXiao Liu, Yanan Zheng, Zhengxiao Du, et al.2021 · 1,039 citations
  11. AdapterFusion: Non-Destructive Task Composition for Transfer LearningJonas Pfeiffer, Aishwarya Kamath, Andreas Rücklé, et al.2020 · 948 citations
  12. Is ChatGPT a General-Purpose Natural Language Processing Task Solver?Chengwei Qin, Aston Zhang, Zhuosheng Zhang, et al.2023 · 818 citations
  13. Calibrate Before Use: Improving Few-Shot Performance of Language ModelsTony Z. Zhao, Eric Wallace, Shi Feng, et al.2021 · 791 citations
  14. Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context LearningHaokun Liu, Derek Tam, Mohammed Muqeeth, et al.2022 · 735 citations
  15. BloombergGPT: A Large Language Model for FinanceShijie Wu, Ozan Irsoy, Steven Lu, et al.2023 · 608 citations
  16. GLaM: Efficient Scaling of Language Models with Mixture-of-ExpertsNan Du, Yanping Huang, Andrew M. Dai, et al.2021 · 474 citations
  17. PPT: Pre-trained Prompt Tuning for Few-shot LearningYuxian Gu, Xu Han, Zhiyuan Liu, et al.2022 · 467 citations
  18. True Few-Shot Learning with Language ModelsEthan Perez, Douwe Kiela, Kyunghyun Cho2021 · 403 citations
  19. The “Problem” of Human Label Variation: On Ground Truth in Data, Modeling and EvaluationBarbara Plank2022 · 364 citations
  20. ERNIE 3.0: Large-scale Knowledge Enhanced Pre-training for Language Understanding and GenerationYu Sun, Shuohuan Wang, Shikun Feng, et al.2021 · 324 citations
  21. How many data points is a prompt worth?Teven Le Scao, Alexander Rush2021 · 315 citations
  22. TIES-Merging: Resolving Interference When Merging ModelsPrateek Yadav, Derek Tam, Leshem Choshen, et al.2023 · 306 citations
  23. SPoT: Better Frozen Model Adaptation through Soft Prompt TransferTu Vu, Brian Lester, Noah Constant, et al.2022 · 300 citations
  24. Want To Reduce Labeling Cost? GPT-3 Can HelpShuohang Wang, Yang Liu, Yichong Xu, et al.2021 · 298 citations
  25. Intermediate-Task Transfer Learning with Pretrained Language Models: When and Why Does It Work?Yada Pruksachatkun, Jason Phang, Haokun Liu, et al.2020 · 279 citations
  26. Surface Form Competition: Why the Highest Probability Answer Isn't Always RightAri Holtzman, Peter West, Vered Shwartz, et al.2021 · 271 citations
  27. Why Can GPT Learn In-Context? Language Models Secretly Perform Gradient Descent as Meta-OptimizersDamai Dai, Yutao Sun, Li Dong, et al.2023 · 252 citations
  28. Adapting Language Models to Compress ContextsAlexis Chevalier, Alexander Wettig, Anirudh Ajith, et al.2023 · 179 citations
  29. Are Natural Language Inference Models IMPPRESsive? Learning IMPlicature and PRESuppositionPaloma Jeretic, Alex Warstadt, Suvrat Bhooshan, et al.2020 · 161 citations
  30. Recent Advances in Natural Language Inference: A Survey of Benchmarks, Resources, and ApproachesShane Storks, Qiaozi Gao, Joyce Y. Chai2019 · 134 citations
  31. To CoT or not to CoT? Chain-of-thought helps mainly on math and symbolic reasoningZayne Sprague, Fangcong Yin, Juan Diego Rodriguez, et al.2024 · 133 citations
  32. Tokenizer Choice For LLM Training: Negligible or Crucial?Mehdi Ali, Michael Fromm, Klaudia Thellmann, et al.2024 · 102 citations
  33. NOPE: A Corpus of Naturally-Occurring Presuppositions in EnglishAlicia Parrish, Sebastian Schuster, Alex Warstadt, et al.2021 · 40 citations
  34. He Thinks He Knows Better than the Doctors: BERT for Event Factuality Fails on PragmaticsNanjiang Jiang, Marie-Catherine de Marneffe2021 · 21 citations
  35. Investigating the Performance of Transformer-Based NLI Models on Presuppositional InferencesJad Kabbara, Jackie Chi Kit Cheung2022 · 12 citations
  36. Finding Common Ground: Annotating and Predicting Common Ground in Spoken ConversationsMagdalena Markowska, Mohammad Taghizadeh, Adil Soubki, et al.2023 · 11 citations
  37. When Your Language Model Cannot Even Do Determiners Right: Probing for Anti-Presuppositions and the Maximize Presupposition! PrincipleJudith Sieker, Sina Zarrieß2023 · 9 citations
  38. SIGA: A Naturalistic NLI Dataset of English Scalar Implicatures with Gradable AdjectivesRashid Nizamani, Sebastian Schuster, Vera Demberg2024 · 8 citations
  39. A Pragmatics-Centered Evaluation Framework for Natural Language UnderstandingDamien Sileo, Philippe Muller, Tim Van de Cruys, et al.2022 · 6 citations

195 more papers of our library cite it; they have no page here yet.

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