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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](https://md.mova.elamur.ai/free/en/docs/papers/enprag-2019-marneffe-commitmentbank-investigating-projection-naturally/)

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1. [Charting the European LLM Benchmarking Landscape: A New Taxonomy and Registry](https://aclanthology.org/2026.llms4ssh-1.22/) *— 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](https://aclanthology.org/2026.acl-long.1046/) *— 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](https://aclanthology.org/2026.loreslm-1.30/) *— 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](https://aclanthology.org/2026.lrec-1.161/) *— 2026 · Proceedings of the Fifteenth Language Resources and Evaluation Conference*
5. [Ultra-Low-Dimensional Prompt Tuning via Random Projection](https://aclanthology.org/2026.eacl-long.59/) *— 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](https://aclanthology.org/2026.findings-acl.1999/) *— 2026 · Findings of the Association for Computational Linguistics: ACL 2026*
7. [A Systematic Survey of Automatic Prompt Optimization Techniques](https://aclanthology.org/2025.emnlp-main.1681/) *— 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](https://aclanthology.org/2025.coling-main.78/) *— 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](https://aclanthology.org/2025.emnlp-main.356/) *— 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](https://aclanthology.org/2025.naacl-long.225/) *— 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](https://aclanthology.org/2025.emnlp-main.358/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing*
12. [FPEdit: Robust LLM Fingerprinting through Localized Parameter Editing](https://arxiv.org/abs/2508.02092) *— 2025 · arXiv:2508.02092*
13. [Harmony in Divergence: Towards Fast, Accurate, and Memory-efficient Zeroth-order LLM Fine-tuning](https://arxiv.org/abs/2502.03304) *— 2025 · arXiv:2502.03304*
14. [Let''s CONFER: A Dataset for Evaluating Natural Language Inference Models on CONditional InFERence and Presupposition](https://arxiv.org/abs/2506.06133) *— 2025 · arXiv:2506.06133*
15. [MEraser: An Effective Fingerprint Erasure Approach for Large Language Models](https://aclanthology.org/2025.acl-long.1455/) *— 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](https://aclanthology.org/2025.findings-acl.107/) *— 2025 · Findings of the Association for Computational Linguistics: ACL 2025*
17. [MoRE: A Mixture of Low-Rank Experts for Adaptive Multi-Task Learning](https://aclanthology.org/2025.findings-acl.68/) *— 2025 · Findings of the Association for Computational Linguistics: ACL 2025*
18. [What Did I Do Wrong? Quantifying LLMs' Sensitivity and Consistency to Prompt Engineering](https://aclanthology.org/2025.naacl-long.73/) *— 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](https://arxiv.org/abs/2503.12668) *— 2025 · arXiv:2503.12668*
20. [Zero-to-Strong Generalization: Eliciting Strong Capabilities of Large Language Models Iteratively without Gold Labels](https://aclanthology.org/2025.coling-main.251/) *— 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](https://arxiv.org/abs/2410.06441) *— 2024 · arXiv:2410.06441*
22. [Calibration-Tuning: Teaching Large Language Models to Know What They Don''t Know](https://aclanthology.org/2024.uncertainlp-1.1/) *— 2024 · Proceedings of the 1st Workshop on Uncertainty-Aware NLP (UncertaiNLP 2024)*
23. [Cataphoric resolution of projective content: The case of occasion verbs](https://semprag.org/index.php/sp/article/view/sp.17.11) *— 2024 · Semantics and Pragmatics*
24. [Contrastive Instruction Tuning](https://aclanthology.org/2024.findings-acl.613/) *— 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](https://aclanthology.org/2024.lrec-main.465/) *— 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](https://aclanthology.org/2024.acl-long.155/) *— 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?](https://aclanthology.org/2024.clpsych-1.3/) *— 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](https://aclanthology.org/2024.acl-long.571/) *— 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](https://aclanthology.org/2024.naacl-long.58/) *— 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](https://aclanthology.org/2024.naacl-long.180/) *— 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](https://arxiv.org/abs/2406.08391) *— 2024 · arXiv:2406.08391*
32. [Multi-Task Learning with Adapters for Plausibility Prediction: Bridging the Gap or Falling into the Trenches?](https://aclanthology.org/2024.insights-1.18/) *— 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](https://aclanthology.org/2024.naacl-long.331/) *— 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](https://aclanthology.org/2024.naacl-long.431/) *— 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](https://aclanthology.org/2024.findings-acl.410/) *— 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](https://aclanthology.org/2024.bucc-1.3/) *— 2024 · Proceedings of the 17th Workshop on Building and Using Comparable Corpora (BUCC) @ LREC-COLING 2024*
37. [Rectifying Demonstration Shortcut in In-Context Learning](https://aclanthology.org/2024.naacl-long.242/) *— 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](https://md.mova.elamur.ai/free/en/docs/papers/nizamani-2024-siga-naturalistic-nli-dataset-english/) *— 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](https://aclanthology.org/2024.mwe-1.22/) *— 2024 · Proceedings of the Joint Workshop on Multiword Expressions and Universal Dependencies (MWE-UD) @ LREC-COLING 2024*
40. [Sinkhorn Distance Minimization for Knowledge Distillation](https://aclanthology.org/2024.lrec-main.1293/) *— 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](https://aclanthology.org/2024.naacl-long.126/) *— 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](https://arxiv.org/abs/2409.12183) *— 2024 · arXiv:2409.12183*
43. [Tokenizer Choice For LLM Training: Negligible or Crucial?](https://aclanthology.org/2024.findings-naacl.247/) *— 2024 · Findings of the Association for Computational Linguistics: NAACL 2024*
44. [Towards More Robust NLP System Evaluation: Handling Missing Scores in Benchmarks](https://aclanthology.org/2024.findings-emnlp.688/) *— 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](https://aclanthology.org/2024.findings-acl.493/) *— 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](https://aclanthology.org/2024.findings-acl.880/) *— 2024 · Findings of the Association for Computational Linguistics: ACL 2024*
47. [Zeroth-Order Fine-Tuning of LLMs with Extreme Sparsity](https://arxiv.org/abs/2406.02913) *— 2024 · arXiv:2406.02913*
48. [tasksource: A Large Collection of NLP tasks with a Structured Dataset Preprocessing Framework](https://aclanthology.org/2024.lrec-main.1361/) *— 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](https://arxiv.org/abs/2307.06435) *— 2023 · arXiv:2307.06435*
50. [A Universal Discriminator for Zero-Shot Generalization](https://aclanthology.org/2023.acl-long.589/) *— 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](https://aclanthology.org/2023.findings-emnlp.334/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
52. [Adapting Language Models to Compress Contexts](https://aclanthology.org/2023.emnlp-main.232/) *— 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](https://aclanthology.org/2023.findings-emnlp.143/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
54. [Automated Few-Shot Classification with Instruction-Finetuned Language Models](https://aclanthology.org/2023.findings-emnlp.158/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
55. [Bipol: Multi-Axes Evaluation of Bias with Explainability in Benchmark Datasets](https://aclanthology.org/2023.ranlp-1.1/) *— 2023 · Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing*
56. [BloombergGPT: A Large Language Model for Finance](https://arxiv.org/abs/2303.17564) *— 2023 · arXiv:2303.17564*
57. [Can Large Language Models Capture Dissenting Human Voices?](https://aclanthology.org/2023.emnlp-main.278/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
58. [ColD Fusion: Collaborative Descent for Distributed Multitask Finetuning](https://aclanthology.org/2023.acl-long.46/) *— 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](https://arxiv.org/abs/2311.13171) *— 2023 · arXiv:2311.13171*
60. [Decomposed Prompt Tuning via Low-Rank Reparameterization](https://aclanthology.org/2023.findings-emnlp.890/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
61. [Deja Vu: Contextual Sparsity for Efficient LLMs at Inference Time](https://arxiv.org/abs/2310.17157) *— 2023 · ICML 2023*
62. [Emergent Modularity in Pre-trained Transformers](https://aclanthology.org/2023.findings-acl.250/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
63. [Evaluating the Robustness of Discrete Prompts](https://aclanthology.org/2023.eacl-main.174/) *— 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](https://arxiv.org/abs/2302.03202) *— 2023 · arXiv:2302.03202*
65. [Exploring the Trade-Offs: Unified Large Language Models vs Local Fine-Tuned Models for Highly-Specific Radiology NLI Task](https://arxiv.org/abs/2304.09138) *— 2023 · arXiv:2304.09138*
66. [Few-shot Adaptation Works with UnpredicTable Data](https://aclanthology.org/2023.acl-long.102/) *— 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](https://aclanthology.org/2023.acl-long.454/) *— 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](https://aclanthology.org/2023.findings-emnlp.551/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
69. [Fine-Tuning Language Models with Just Forward Passes](https://arxiv.org/abs/2305.17333) *— 2023 · arXiv:2305.17333*
70. [Generative Calibration for In-context Learning](https://aclanthology.org/2023.findings-emnlp.152/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
71. [Improving Few-Shot Generalization by Exploring and Exploiting Auxiliary Data](https://arxiv.org/abs/2302.00674) *— 2023 · arXiv:2302.00674*
72. [Is ChatGPT a General-Purpose Natural Language Processing Task Solver?](https://arxiv.org/abs/2302.06476) *— 2023 · arXiv:2302.06476*
73. [Knowledge is a Region in Weight Space for Fine-tuned Language Models](https://aclanthology.org/2023.findings-emnlp.95/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
74. [Learning to Initialize: Can Meta Learning Improve Cross-task Generalization in Prompt Tuning?](https://aclanthology.org/2023.acl-long.659/) *— 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](https://aclanthology.org/2023.acl-long.783/) *— 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](https://aclanthology.org/2023.acl-long.48/) *— 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](https://aclanthology.org/2023.acl-long.867/) *— 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](https://aclanthology.org/2023.emnlp-main.335/) *— 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](https://aclanthology.org/2023.conll-1.9/) *— 2023 · Proceedings of the 27th Conference on Computational Natural Language Learning (CoNLL)*
80. [Parallel Context Windows for Large Language Models](https://aclanthology.org/2023.acl-long.352/) *— 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](https://aclanthology.org/2023.findings-emnlp.584/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
82. [Parameter-efficient Tuning for Large Language Model without Calculating Its Gradients](https://aclanthology.org/2023.emnlp-main.22/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
83. [Pre-Training to Learn in Context](https://aclanthology.org/2023.acl-long.267/) *— 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](https://arxiv.org/abs/2310.16427) *— 2023 · arXiv:2310.16427*
85. [Prototype-based HyperAdapter for Sample-Efficient Multi-task Tuning](https://aclanthology.org/2023.emnlp-main.280/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
86. [Pushing the Limits of ChatGPT on NLP Tasks](https://arxiv.org/abs/2306.09719) *— 2023 · arXiv:2306.09719*
87. [Representative Demonstration Selection for In-Context Learning with Two-Stage Determinantal Point Process](https://aclanthology.org/2023.emnlp-main.331/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
88. [Residual Prompt Tuning: improving prompt tuning with residual reparameterization](https://aclanthology.org/2023.findings-acl.421/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
89. [Revisiting Token Dropping Strategy in Efficient BERT Pretraining](https://aclanthology.org/2023.acl-long.579/) *— 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](https://aclanthology.org/2023.semeval-1.137/) *— 2023 · Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)*
91. [Scalable Prompt Generation for Semi-supervised Learning with Language Models](https://aclanthology.org/2023.findings-eacl.58/) *— 2023 · Findings of the Association for Computational Linguistics: EACL 2023*
92. [Scaling Data-Constrained Language Models](https://arxiv.org/abs/2305.16264) *— 2023 · arXiv:2305.16264*
93. [Self-Evolution Learning for Discriminative Language Model Pretraining](https://aclanthology.org/2023.findings-acl.254/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
94. [Symbolic Discovery of Optimization Algorithms](https://arxiv.org/abs/2302.06675) *— 2023 · arXiv:2302.06675*
95. [TIES-Merging: Resolving Interference When Merging Models](https://arxiv.org/abs/2306.01708) *— 2023 · arXiv:2306.01708*
96. [TaskWeb: Selecting Better Source Tasks for Multi-task NLP](https://aclanthology.org/2023.emnlp-main.680/) *— 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](https://arxiv.org/abs/2306.01116) *— 2023 · arXiv:2306.01116*
98. [The language of prompting: What linguistic properties make a prompt successful?](https://aclanthology.org/2023.findings-emnlp.618/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
99. [Towards Generative Event Factuality Prediction](https://aclanthology.org/2023.findings-acl.44/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
100. [Towards Informative Few-Shot Prompt with Maximum Information Gain for In-Context Learning](https://aclanthology.org/2023.findings-emnlp.1060/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*

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