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

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

Pages: 1 · 2 · 3

  1. Charting the European LLM Benchmarking Landscape: A New Taxonomy and Registry — 2026 · Proceedings of Shaping Multilingual, Multimodal AI for the Social Sciences and Humanities (LLMs4SSH) @ LREC 2026
  2. SADA: Bridging In-Context Learning and Fine-Tuning via State-Aligned Distillation Adapters — 2026 · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
  3. Serbian SuperGLUE: Towards an Evaluation Benchmark for South Slavic Language Models — 2026 · Proceedings of the Second Workshop on Language Models for Low-Resource Languages (LoResLM 2026)
  4. There Is No Spoon: Existential Presupposition in Large Language Models — 2026 · Proceedings of the Fifteenth Language Resources and Evaluation Conference
  5. Ultra-Low-Dimensional Prompt Tuning via Random Projection — 2026 · Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)
  6. Universally Empowering Zeroth-Order Optimization via Adaptive Layer-wise Sampling — 2026 · Findings of the Association for Computational Linguistics: ACL 2026
  7. A Systematic Survey of Automatic Prompt Optimization Techniques — 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
  8. BiLD: Bi-directional Logits Difference Loss for Large Language Model Distillation — 2025 · Proceedings of the 31st International Conference on Computational Linguistics
  9. CTCC: A Robust and Stealthy Fingerprinting Framework for Large Language Models via Cross-Turn Contextual Correlation Backdoor — 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
  10. Efficient and Effective Prompt Tuning via Prompt Decomposition and Compressed Outer Product — 2025 · Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
  11. EverTracer: Hunting Stolen Large Language Models via Stealthy and Robust Probabilistic Fingerprint — 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
  12. FPEdit: Robust LLM Fingerprinting through Localized Parameter Editing — 2025 · arXiv:2508.02092
  13. Harmony in Divergence: Towards Fast, Accurate, and Memory-efficient Zeroth-order LLM Fine-tuning — 2025 · arXiv:2502.03304
  14. Let''s CONFER: A Dataset for Evaluating Natural Language Inference Models on CONditional InFERence and Presupposition — 2025 · arXiv:2506.06133
  15. MEraser: An Effective Fingerprint Erasure Approach for Large Language Models — 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
  16. Measuring Bias and Agreement in Large Language Model Presupposition Judgments — 2025 · Findings of the Association for Computational Linguistics: ACL 2025
  17. MoRE: A Mixture of Low-Rank Experts for Adaptive Multi-Task Learning — 2025 · Findings of the Association for Computational Linguistics: ACL 2025
  18. What Did I Do Wrong? Quantifying LLMs' Sensitivity and Consistency to Prompt Engineering — 2025 · Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
  19. ZO2: Scalable Zeroth-Order Fine-Tuning for Extremely Large Language Models with Limited GPU Memory — 2025 · arXiv:2503.12668
  20. Zero-to-Strong Generalization: Eliciting Strong Capabilities of Large Language Models Iteratively without Gold Labels — 2025 · Proceedings of the 31st International Conference on Computational Linguistics
  21. Addax: Utilizing Zeroth-Order Gradients to Improve Memory Efficiency and Performance of SGD for Fine-Tuning Language Models — 2024 · arXiv:2410.06441
  22. Calibration-Tuning: Teaching Large Language Models to Know What They Don''t Know — 2024 · Proceedings of the 1st Workshop on Uncertainty-Aware NLP (UncertaiNLP 2024)
  23. Cataphoric resolution of projective content: The case of occasion verbs — 2024 · Semantics and Pragmatics
  24. Contrastive Instruction Tuning — 2024 · Findings of the Association for Computational Linguistics: ACL 2024
  25. Do Language Models Care about Text Quality? Evaluating Web-Crawled Corpora across 11 Languages — 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
  26. Enhancing In-Context Learning via Implicit Demonstration Augmentation — 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
  27. How Can Client Motivational Language Inform Psychotherapy Agents? — 2024 · Proceedings of the 9th Workshop on Computational Linguistics and Clinical Psychology (CLPsych 2024)
  28. HyperMoE: Towards Better Mixture of Experts via Transferring Among Experts — 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
  29. In-context Learning and Gradient Descent Revisited — 2024 · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
  30. Instructional Fingerprinting of Large Language Models — 2024 · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
  31. Large Language Models Must Be Taught to Know What They Don't Know — 2024 · arXiv:2406.08391
  32. Multi-Task Learning with Adapters for Plausibility Prediction: Bridging the Gap or Falling into the Trenches? — 2024 · Proceedings of the Fifth Workshop on Insights from Negative Results in NLP
  33. NLP Systems That Can't Tell Use from Mention Censor Counterspeech, but Teaching the Distinction Helps — 2024 · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
  34. Naive Bayes-based Context Extension for Large Language Models — 2024 · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
  35. PEMT: Multi-Task Correlation Guided Mixture-of-Experts Enables Parameter-Efficient Transfer Learning — 2024 · Findings of the Association for Computational Linguistics: ACL 2024
  36. PORTULAN ExtraGLUE Datasets and Models: Kick-starting a Benchmark for the Neural Processing of Portuguese — 2024 · Proceedings of the 17th Workshop on Building and Using Comparable Corpora (BUCC) @ LREC-COLING 2024
  37. Rectifying Demonstration Shortcut in In-Context Learning — 2024 · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
  38. SIGA: A Naturalistic NLI Dataset of English Scalar Implicatures with Gradable Adjectives — our card · 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
  39. Sign of the Times: Evaluating the use of Large Language Models for Idiomaticity Detection — 2024 · Proceedings of the Joint Workshop on Multiword Expressions and Universal Dependencies (MWE-UD) @ LREC-COLING 2024
  40. Sinkhorn Distance Minimization for Knowledge Distillation — 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
  41. The Perspectivist Paradigm Shift: Assumptions and Challenges of Capturing Human Labels — 2024 · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
  42. To CoT or not to CoT? Chain-of-thought helps mainly on math and symbolic reasoning — 2024 · arXiv:2409.12183
  43. Tokenizer Choice For LLM Training: Negligible or Crucial? — 2024 · Findings of the Association for Computational Linguistics: NAACL 2024
  44. Towards More Robust NLP System Evaluation: Handling Missing Scores in Benchmarks — 2024 · Findings of the Association for Computational Linguistics: EMNLP 2024
  45. Towards Unified Task Embeddings Across Multiple Models: Bridging the Gap for Prompt-Based Large Language Models and Beyond — 2024 · Findings of the Association for Computational Linguistics: ACL 2024
  46. Views Are My Own, but Also Yours: Benchmarking Theory of Mind Using Common Ground — 2024 · Findings of the Association for Computational Linguistics: ACL 2024
  47. Zeroth-Order Fine-Tuning of LLMs with Extreme Sparsity — 2024 · arXiv:2406.02913
  48. tasksource: A Large Collection of NLP tasks with a Structured Dataset Preprocessing Framework — 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
  49. A Comprehensive Overview of Large Language Models — 2023 · arXiv:2307.06435
  50. A Universal Discriminator for Zero-Shot Generalization — 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
  51. Active Learning Principles for In-Context Learning with Large Language Models — 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023
  52. Adapting Language Models to Compress Contexts — 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
  53. Attack Prompt Generation for Red Teaming and Defending Large Language Models — 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023
  54. Automated Few-Shot Classification with Instruction-Finetuned Language Models — 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023
  55. Bipol: Multi-Axes Evaluation of Bias with Explainability in Benchmark Datasets — 2023 · Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing
  56. BloombergGPT: A Large Language Model for Finance — 2023 · arXiv:2303.17564
  57. Can Large Language Models Capture Dissenting Human Voices? — 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
  58. ColD Fusion: Collaborative Descent for Distributed Multitask Finetuning — 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
  59. ComPEFT: Compression for Communicating Parameter Efficient Updates via Sparsification and Quantization — 2023 · arXiv:2311.13171
  60. Decomposed Prompt Tuning via Low-Rank Reparameterization — 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023
  61. Deja Vu: Contextual Sparsity for Efficient LLMs at Inference Time — 2023 · ICML 2023
  62. Emergent Modularity in Pre-trained Transformers — 2023 · Findings of the Association for Computational Linguistics: ACL 2023
  63. Evaluating the Robustness of Discrete Prompts — 2023 · Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics
  64. Exploring the Benefits of Training Expert Language Models over Instruction Tuning — 2023 · arXiv:2302.03202
  65. Exploring the Trade-Offs: Unified Large Language Models vs Local Fine-Tuned Models for Highly-Specific Radiology NLI Task — 2023 · arXiv:2304.09138
  66. Few-shot Adaptation Works with UnpredicTable Data — 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
  67. FiD-ICL: A Fusion-in-Decoder Approach for Efficient In-Context Learning — 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
  68. Finding Common Ground: Annotating and Predicting Common Ground in Spoken Conversations — 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023
  69. Fine-Tuning Language Models with Just Forward Passes — 2023 · arXiv:2305.17333
  70. Generative Calibration for In-context Learning — 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023
  71. Improving Few-Shot Generalization by Exploring and Exploiting Auxiliary Data — 2023 · arXiv:2302.00674
  72. Is ChatGPT a General-Purpose Natural Language Processing Task Solver? — 2023 · arXiv:2302.06476
  73. Knowledge is a Region in Weight Space for Fine-tuned Language Models — 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023
  74. Learning to Initialize: Can Meta Learning Improve Cross-task Generalization in Prompt Tuning? — 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
  75. Mitigating Label Biases for In-context Learning — 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
  76. Multi-CLS BERT: An Efficient Alternative to Traditional Ensembling — 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
  77. NOTABLE: Transferable Backdoor Attacks Against Prompt-based NLP Models — 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
  78. Navigating the Grey Area: How Expressions of Uncertainty and Overconfidence Affect Language Models — 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
  79. PROPRES: Investigating the Projectivity of Presupposition with Various Triggers and Environments — 2023 · Proceedings of the 27th Conference on Computational Natural Language Learning (CoNLL)
  80. Parallel Context Windows for Large Language Models — 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
  81. Parameter Efficient Multi-task Fine-tuning by Learning to Transfer Token-wise Prompts — 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023
  82. Parameter-efficient Tuning for Large Language Model without Calculating Its Gradients — 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
  83. Pre-Training to Learn in Context — 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
  84. PromptAgent: Strategic Planning with Language Models Enables Expert-level Prompt Optimization — 2023 · arXiv:2310.16427
  85. Prototype-based HyperAdapter for Sample-Efficient Multi-task Tuning — 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
  86. Pushing the Limits of ChatGPT on NLP Tasks — 2023 · arXiv:2306.09719
  87. Representative Demonstration Selection for In-Context Learning with Two-Stage Determinantal Point Process — 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
  88. Residual Prompt Tuning: improving prompt tuning with residual reparameterization — 2023 · Findings of the Association for Computational Linguistics: ACL 2023
  89. Revisiting Token Dropping Strategy in Efficient BERT Pretraining — 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
  90. Saama AI Research at SemEval-2023 Task 7: Exploring the Capabilities of Flan-T5 for Multi-evidence Natural Language Inference in Clinical Trial Data — 2023 · Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)
  91. Scalable Prompt Generation for Semi-supervised Learning with Language Models — 2023 · Findings of the Association for Computational Linguistics: EACL 2023
  92. Scaling Data-Constrained Language Models — 2023 · arXiv:2305.16264
  93. Self-Evolution Learning for Discriminative Language Model Pretraining — 2023 · Findings of the Association for Computational Linguistics: ACL 2023
  94. Symbolic Discovery of Optimization Algorithms — 2023 · arXiv:2302.06675
  95. TIES-Merging: Resolving Interference When Merging Models — 2023 · arXiv:2306.01708
  96. TaskWeb: Selecting Better Source Tasks for Multi-task NLP — 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
  97. The RefinedWeb Dataset for Falcon LLM: Outperforming Curated Corpora with Web Data, and Web Data Only — 2023 · arXiv:2306.01116
  98. The language of prompting: What linguistic properties make a prompt successful? — 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023
  99. Towards Generative Event Factuality Prediction — 2023 · Findings of the Association for Computational Linguistics: ACL 2023
  100. Towards Informative Few-Shot Prompt with Maximum Information Gain for In-Context Learning — 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023

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