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

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  1. ComPEFT: Compression for Communicating Parameter Efficient Updates via Sparsification and Quantization — 2023 · arXiv:2311.13171
  2. Decomposed Prompt Tuning via Low-Rank Reparameterization — 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023
  3. Deja Vu: Contextual Sparsity for Efficient LLMs at Inference Time — 2023 · ICML 2023
  4. Effectiveness of Data Augmentation for Parameter Efficient Tuning with Limited Data — 2023 · Proceedings of the 8th Workshop on Representation Learning for NLP (RepL4NLP 2023)
  5. Efficiently Enhancing Zero-Shot Performance of Instruction Following Model via Retrieval of Soft Prompt — 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023
  6. Emergent Modularity in Pre-trained Transformers — 2023 · Findings of the Association for Computational Linguistics: ACL 2023
  7. Evaluating the Robustness of Discrete Prompts — 2023 · Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics
  8. Exploring the Benefits of Training Expert Language Models over Instruction Tuning — 2023 · arXiv:2302.03202
  9. Exploring the Trade-Offs: Unified Large Language Models vs Local Fine-Tuned Models for Highly-Specific Radiology NLI Task — 2023 · arXiv:2304.09138
  10. FMI-SU at SemEval-2023 Task 7: Two-level Entailment Classification of Clinical Trials Enhanced by Contextual Data Augmentation — 2023 · Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)
  11. Few-shot Adaptation Works with UnpredicTable Data — 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
  12. 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)
  13. Finding Common Ground: Annotating and Predicting Common Ground in Spoken Conversations — 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023
  14. Fine-Tuning Language Models with Just Forward Passes — 2023 · arXiv:2305.17333
  15. Flatness-Aware Prompt Selection Improves Accuracy and Sample Efficiency — 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023
  16. Generative Calibration for In-context Learning — 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023
  17. Improving Few-Shot Generalization by Exploring and Exploiting Auxiliary Data — 2023 · arXiv:2302.00674
  18. Improving Input-label Mapping with Demonstration Replay for In-context Learning — 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023
  19. Is ChatGPT a General-Purpose Natural Language Processing Task Solver? — 2023 · arXiv:2302.06476
  20. Knowledge is a Region in Weight Space for Fine-tuned Language Models — 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023
  21. 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)
  22. Learning to Predict Task Transferability via Soft Prompt — 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
  23. Mitigating Label Biases for In-context Learning — 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
  24. 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)
  25. 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)
  26. 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
  27. PROPRES: Investigating the Projectivity of Presupposition with Various Triggers and Environments — 2023 · Proceedings of the 27th Conference on Computational Natural Language Learning (CoNLL)
  28. Parallel Context Windows for Large Language Models — 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
  29. Parameter Efficient Multi-task Fine-tuning by Learning to Transfer Token-wise Prompts — 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023
  30. Parameter-efficient Tuning for Large Language Model without Calculating Its Gradients — 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
  31. Pre-Training to Learn in Context — 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
  32. PromptAgent: Strategic Planning with Language Models Enables Expert-level Prompt Optimization — 2023 · arXiv:2310.16427
  33. Prototype-based HyperAdapter for Sample-Efficient Multi-task Tuning — 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
  34. Pushing the Limits of ChatGPT on NLP Tasks — 2023 · arXiv:2306.09719
  35. 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
  36. Residual Prompt Tuning: improving prompt tuning with residual reparameterization — 2023 · Findings of the Association for Computational Linguistics: ACL 2023
  37. Retrieval-based Knowledge Transfer: An Effective Approach for Extreme Large Language Model Compression — 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023
  38. 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)
  39. Revisiting k-NN for Fine-tuning Pre-trained Language Models — 2023 · Proceedings of the 22nd Chinese National Conference on Computational Linguistics
  40. 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)
  41. Scalable Prompt Generation for Semi-supervised Learning with Language Models — 2023 · Findings of the Association for Computational Linguistics: EACL 2023
  42. Scaling Data-Constrained Language Models — 2023 · arXiv:2305.16264
  43. Self-Evolution Learning for Discriminative Language Model Pretraining — 2023 · Findings of the Association for Computational Linguistics: ACL 2023
  44. Self-supervised Meta-Prompt Learning with Meta-Gradient Regularization for Few-shot Generalization — 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023
  45. SharPT: Shared Latent Space Prompt Tuning — 2023 · Findings of the Association for Computational Linguistics: EACL 2023
  46. Symbolic Discovery of Optimization Algorithms — 2023 · arXiv:2302.06675
  47. TIES-Merging: Resolving Interference When Merging Models — 2023 · arXiv:2306.01708
  48. TaskWeb: Selecting Better Source Tasks for Multi-task NLP — 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
  49. The RefinedWeb Dataset for Falcon LLM: Outperforming Curated Corpora with Web Data, and Web Data Only — 2023 · arXiv:2306.01116
  50. The language of prompting: What linguistic properties make a prompt successful? — 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023
  51. Towards Generative Event Factuality Prediction — 2023 · Findings of the Association for Computational Linguistics: ACL 2023
  52. Towards Informative Few-Shot Prompt with Maximum Information Gain for In-Context Learning — 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023
  53. Trained on 100 million words and still in shape: BERT meets British National Corpus — 2023 · Findings of the Association for Computational Linguistics: EACL 2023
  54. Tree Prompting: Efficient Task Adaptation without Fine-Tuning — 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
  55. Two Examples are Better than One: Context Regularization for Gradient-based Prompt Tuning — 2023 · Findings of the Association for Computational Linguistics: ACL 2023
  56. 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
  57. 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
  58. Where to start? Analyzing the potential value of intermediate models — 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
  59. 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
  60. You Are What You Annotate: Towards Better Models through Annotator Representations — 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023
  61. Zemi: Learning Zero-Shot Semi-Parametric Language Models from Multiple Tasks — 2023 · Findings of the Association for Computational Linguistics: ACL 2023
  62. kNN-CM: A Non-parametric Inference-Phase Adaptation of Parametric Text Classifiers — 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023
  63. A Pragmatics-Centered Evaluation Framework for Natural Language Understanding — 2022 · Proceedings of the Thirteenth Language Resources and Evaluation Conference
  64. Analyzing the Effects of Annotator Gender across NLP Tasks — 2022 · Proceedings of the 1st Workshop on Perspectivist Approaches to NLP @LREC2022
  65. Ask Me Anything: A simple strategy for prompting language models — 2022 · arXiv:2210.02441
  66. 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
  67. 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)
  68. Eliciting and Understanding Cross-task Skills with Task-level Mixture-of-Experts — 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022
  69. 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)
  70. Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning — 2022 · arXiv:2205.05638
  71. 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)
  72. 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)
  73. 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
  74. Ground-Truth Labels Matter: A Deeper Look into Input-Label Demonstrations — 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
  75. Improving Sharpness-Aware Minimization with Fisher Mask for Better Generalization on Language Models — 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022
  76. Improving the Sample Efficiency of Prompt Tuning with Domain Adaptation — 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022
  77. Investigating the Performance of Transformer-Based NLI Models on Presuppositional Inferences — 2022 · Proceedings of the 29th International Conference on Computational Linguistics
  78. Learning Instructions with Unlabeled Data for Zero-Shot Cross-Task Generalization — 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
  79. Model Cascading: Towards Jointly Improving Efficiency and Accuracy of NLP Systems — 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
  80. Nearest Neighbor Zero-Shot Inference — 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
  81. On Measuring the Intrinsic Few-Shot Hardness of Datasets — 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
  82. 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)
  83. Probing as Quantifying Inductive Bias — 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
  84. Prompt Consistency for Zero-Shot Task Generalization — 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022
  85. 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)
  86. Re-Examining FactBank: Predicting the Author''s Presentation of Factuality — 2022 · Proceedings of the 29th International Conference on Computational Linguistics
  87. Rethinking the Role of Demonstrations: What Makes In-Context Learning Work? — 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
  88. Revisiting Parameter-Efficient Tuning: Are We Really There Yet? — 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
  89. 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)
  90. ST-MoE: Designing Stable and Transferable Sparse Expert Models — 2022 · arXiv:2202.08906
  91. Task Compass: Scaling Multi-task Pre-training with Task Prefix — 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022
  92. 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
  93. 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
  94. Transferring Confluent Knowledge to Argument Mining — 2022 · Proceedings of the 29th International Conference on Computational Linguistics
  95. Vector-Quantized Input-Contextualized Soft Prompts for Natural Language Understanding — 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
  96. 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)
  97. 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
  98. Beyond Reptile: Meta-Learned Dot-Product Maximization between Gradients for Improved Single-Task Regularization — 2021 · Findings of the Association for Computational Linguistics: EMNLP 2021
  99. Calibrate Before Use: Improving Few-Shot Performance of Language Models — 2021 · arXiv:2102.09690
  100. 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)

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