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home / docs / papers / cited-by / Who cites «The CommitmentBank: Investigating projection in naturally occurring discourse» · page 2

Who cites «The CommitmentBank: Investigating projection in naturally occurring discourse» · page 2

234 papers of our library cite it, newest first: found in their bibliographies, which we read ourselves. ← the card

Pages: 1 · 2 · 3

  1. Trained on 100 million words and still in shape: BERT meets British National Corpus — 2023 · Findings of the Association for Computational Linguistics: EACL 2023
  2. Tree Prompting: Efficient Task Adaptation without Fine-Tuning — 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
  3. Understanding the Role of Input Token Characters in Language Models: How Does Information Loss Affect Performance? — 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
  4. When Your Language Model Cannot Even Do Determiners Right: Probing for Anti-Presuppositions and the Maximize Presupposition! Principle — our card · 2023 · Proceedings of the 6th BlackboxNLP Workshop: Analyzing and Interpreting Neural Networks for NLP
  5. Where to start? Analyzing the potential value of intermediate models — 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
  6. Why Can GPT Learn In-Context? Language Models Secretly Perform Gradient Descent as Meta-Optimizers — 2023 · Findings of the Association for Computational Linguistics: ACL 2023
  7. You Are What You Annotate: Towards Better Models through Annotator Representations — 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023
  8. Zemi: Learning Zero-Shot Semi-Parametric Language Models from Multiple Tasks — 2023 · Findings of the Association for Computational Linguistics: ACL 2023
  9. A Pragmatics-Centered Evaluation Framework for Natural Language Understanding — 2022 · Proceedings of the Thirteenth Language Resources and Evaluation Conference
  10. Analyzing the Effects of Annotator Gender across NLP Tasks — 2022 · Proceedings of the 1st Workshop on Perspectivist Approaches to NLP @LREC2022
  11. Ask Me Anything: A simple strategy for prompting language models — 2022 · arXiv:2210.02441
  12. Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models — 2022 · arXiv:2206.04615 · Transactions on Machine Learning Research, May/2022, https://openreview.net/forum?id=uyTL5Bvosj
  13. Coherence boosting: When your pretrained language model is not paying enough attention — 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
  14. Eliciting and Understanding Cross-task Skills with Task-level Mixture-of-Experts — 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022
  15. Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity — 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
  16. Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning — 2022 · arXiv:2205.05638
  17. FewNLU: Benchmarking State-of-the-Art Methods for Few-Shot Natural Language Understanding — 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
  18. FlipDA: Effective and Robust Data Augmentation for Few-Shot Learning — 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
  19. Foundation Models of Scientific Knowledge for Chemistry: Opportunities, Challenges and Lessons Learned — 2022 · Proceedings of BigScience Episode \#5 – Workshop on Challenges & Perspectives in Creating Large Language Models
  20. Ground-Truth Labels Matter: A Deeper Look into Input-Label Demonstrations — 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
  21. Improving Sharpness-Aware Minimization with Fisher Mask for Better Generalization on Language Models — 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022
  22. Improving the Sample Efficiency of Prompt Tuning with Domain Adaptation — 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022
  23. Investigating the Performance of Transformer-Based NLI Models on Presuppositional Inferences — 2022 · Proceedings of the 29th International Conference on Computational Linguistics
  24. Learning Instructions with Unlabeled Data for Zero-Shot Cross-Task Generalization — 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
  25. Model Cascading: Towards Jointly Improving Efficiency and Accuracy of NLP Systems — 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
  26. Nearest Neighbor Zero-Shot Inference — 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
  27. On Measuring the Intrinsic Few-Shot Hardness of Datasets — 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
  28. PPT: Pre-trained Prompt Tuning for Few-shot Learning — 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
  29. Probing as Quantifying Inductive Bias — 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
  30. Prompt Consistency for Zero-Shot Task Generalization — 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022
  31. Prompt-free and Efficient Few-shot Learning with Language Models — 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
  32. Re-Examining FactBank: Predicting the Author''s Presentation of Factuality — 2022 · Proceedings of the 29th International Conference on Computational Linguistics
  33. Rethinking the Role of Demonstrations: What Makes In-Context Learning Work? — 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
  34. Revisiting Parameter-Efficient Tuning: Are We Really There Yet? — 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
  35. SPoT: Better Frozen Model Adaptation through Soft Prompt Transfer — 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
  36. ST-MoE: Designing Stable and Transferable Sparse Expert Models — 2022 · arXiv:2202.08906
  37. Task Compass: Scaling Multi-task Pre-training with Task Prefix — 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022
  38. The ``Problem'''' of Human Label Variation: On Ground Truth in Data, Modeling and Evaluation — 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
  39. Tiny-Attention Adapter: Contexts Are More Important Than the Number of Parameters — 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
  40. Transferring Confluent Knowledge to Argument Mining — 2022 · Proceedings of the 29th International Conference on Computational Linguistics
  41. Vector-Quantized Input-Contextualized Soft Prompts for Natural Language Understanding — 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
  42. Word Order Does Matter and Shuffled Language Models Know It — 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
  43. Zero-Shot Learners for Natural Language Understanding via a Unified Multiple Choice Perspective — 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
  44. Beyond Reptile: Meta-Learned Dot-Product Maximization between Gradients for Improved Single-Task Regularization — 2021 · Findings of the Association for Computational Linguistics: EMNLP 2021
  45. Calibrate Before Use: Improving Few-Shot Performance of Language Models — 2021 · arXiv:2102.09690
  46. Comparing Test Sets with Item Response Theory — 2021 · 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)
  47. CrossFit: A Few-shot Learning Challenge for Cross-task Generalization in NLP — 2021 · arXiv:2104.08835
  48. ERNIE 3.0: Large-scale Knowledge Enhanced Pre-training for Language Understanding and Generation — 2021 · arXiv:2107.02137
  49. ExT5: Towards Extreme Multi-Task Scaling for Transfer Learning — 2021 · arXiv:2111.10952
  50. Finetuned Language Models Are Zero-Shot Learners — 2021 · arXiv:2109.01652
  51. GLaM: Efficient Scaling of Language Models with Mixture-of-Experts — 2021 · arXiv:2112.06905
  52. GPT Understands, Too — 2021 · arXiv:2103.10385
  53. He Thinks He Knows Better than the Doctors: BERT for Event Factuality Fails on Pragmatics — our card · 2021
  54. How many data points is a prompt worth? — 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
  55. Identifying inherent disagreement in natural language inference — 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
  56. Issues with Entailment-based Zero-shot Text Classification — 2021 · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)
  57. It''s Not Just Size That Matters: Small Language Models Are Also Few-Shot Learners — 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
  58. Learn Continually, Generalize Rapidly: Lifelong Knowledge Accumulation for Few-shot Learning — 2021 · Findings of the Association for Computational Linguistics: EMNLP 2021
  59. MetaICL: Learning to Learn In Context — 2021 · arXiv:2110.15943
  60. NOPE: A Corpus of Naturally-Occurring Presuppositions in English — our card · 2021 · Proceedings of the 25th Conference on Computational Natural Language Learning
  61. Surface Form Competition: Why the Highest Probability Answer Isn''t Always Right — 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
  62. The Power of Scale for Parameter-Efficient Prompt Tuning — 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
  63. True Few-Shot Learning with Language Models — 2021 · arXiv:2105.11447
  64. UNICORN on RAINBOW: A Universal Commonsense Reasoning Model on a New Multitask Benchmark — 2021 · arXiv:2103.13009
  65. WARP: Word-level Adversarial ReProgramming — 2021 · arXiv:2101.00121
  66. Want To Reduce Labeling Cost? GPT-3 Can Help — 2021 · Findings of the Association for Computational Linguistics: EMNLP 2021
  67. AdapterFusion: Non-Destructive Task Composition for Transfer Learning — 2020 · arXiv:2005.00247 · Proceedings of EACL 2021
  68. Are Natural Language Inference Models IMPPRESsive? Learning IMPlicature and PRESupposition — our card · 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
  69. DeBERTa: Decoding-enhanced BERT with Disentangled Attention — 2020 · arXiv:2006.03654
  70. Intermediate-Task Transfer Learning with Pretrained Language Models: When and Why Does It Work? — 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
  71. Language Models are Few-Shot Learners — 2020 · arXiv:2005.14165
  72. New Protocols and Negative Results for Textual Entailment Data Collection — 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
  73. Which *BERT? A Survey Organizing Contextualized Encoders — 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
  74. Do You Know That Florence Is Packed with Visitors? Evaluating State-of-the-art Models of Speaker Commitment — 2019 · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
  75. Evaluating BERT for natural language inference: A case study on the CommitmentBank — 2019 · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
  76. Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer — 2019 · arXiv:1910.10683
  77. Recent Advances in Natural Language Inference: A Survey of Benchmarks, Resources, and Approaches — 2019 · arXiv:1904.01172
  78. SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems — 2019 · arXiv:1905.00537
  79. azin-2026-do-language-models-know-theo
  80. belanec-2026-peft-bench-parameter-efficient-fine
  81. belanec-2026-peft-factory-unified-parameter-efficient
  82. bhardwaj-2023-knn-cm-non-parametric-inference
  83. feng-2023-learning-predict-task-transferability-soft
  84. geng-2025-great-memory-shallow-reasoning-limits
  85. gong-2023-improving-input-label-mapping-demonstration
  86. gozzi-2025-bidirectional-emotional-influence-human-llm
  87. groeneveld-2023-catwalk-unified-language-model-evaluation
  88. ha-2023-two-examples-are-better-than
  89. hejabi-2026-flip-flop-consistency-unsupervised-training
  90. khanmohammadi-2025-calibrating-llm-confidence-probing-perturbed
  91. kim-2025-first-attentions-last-better-exploiting
  92. kuang-2024-multi-scale-prompt-memory-augmented
  93. l-2024-hire-high-recall-approximate-top
  94. lee-2023-bayesian-multi-task-transfer-learning
  95. li-2023-revisiting-k-nn-fine-tuning
  96. li-2024-feature-adaptive-data-scalable-context
  97. li-2024-lorasc-expressive-generalizable-low-rank
  98. li-2025-glider-global-local-instruction-driven
  99. li-2025-task-calibration-calibrating-large-language
  100. lin-2024-accept-adaptive-codebook-composite-efficient

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