mlsanityData to 7 Oct 2026

Automatically Identifying Words That Can Serve as Labels for Few-Shot Text Classification

arXiv 2010.13641 ↗First posted 26 Oct 2020cs.CL cs.AI cs.LGcited by 98

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Abstract (arXiv)

A recent approach for few-shot text classification is to convert textual inputs to cloze questions that contain some form of task description, process them with a pretrained language model and map the predicted words to labels. Manually defining this mapping between words and labels requires both domain expertise and an understanding of the language model's abilities. To mitigate this issue, we devise an approach that automatically finds such a mapping given small amounts of training data. For a number of tasks, the mapping found by our approach performs almost as well as hand-crafted label-to-word mappings.

Key facts. Cited by 98 papers in the map (data through 7 Oct 2026). First posted on arXiv 26 Oct 2020.

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Citing papers per year: 2021: 18; 2022: 34; 2023: 32; 2024: 9; 2025: 4; 2026: 1.

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  1. 2021Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processingcited by 2,222
  2. 2021Recent Advances in Natural Language Processing via Large Pre-Trained Language Models: A Surveycited by 457
  3. 2022RLPrompt: Optimizing Discrete Text Prompts with Reinforcement Learningcited by 361
  4. 2022Large Language Models Are Reasoning Teacherscited by 318
  5. 2022Black-Box Tuning for Language-Model-as-a-Servicecited by 254
  6. 2021PTR: Prompt Tuning with Rules for Text Classificationcited by 238
  7. 2021How Many Data Points is a Prompt Worth?cited by 197
  8. 2022On Second Thought, Let's Not Think Step by Step! Bias and Toxicity in Zero-Shot Reasoningcited by 191
  9. 2021Knowledgeable Prompt-tuning: Incorporating Knowledge into Prompt Verbalizer for Text Classificationcited by 183
  10. 2021KnowPrompt: Knowledge-aware Prompt-tuning with Synergistic Optimization for Relation Extractioncited by 180
  11. 2021Challenges in Detoxifying Language Modelscited by 169
  12. 2023A Survey of Large Language Models for Healthcare: from Data, Technology, and Applications to Accountability and Ethicscited by 162
38 more
  1. 2021Template-Based Named Entity Recognition Using BARTcited by 153
  2. 2023Few-shot Fine-tuning vs. In-context Learning: A Fair Comparison and Evaluationcited by 134
  3. 2022Thinking about GPT-3 In-Context Learning for Biomedical IE? Think Againcited by 83
  4. 2021Prompt-Learning for Fine-Grained Entity Typingcited by 62
  5. 2022Prototypical Verbalizer for Prompt-based Few-shot Tuningcited by 53
  6. 2022BBTv2: Towards a Gradient-Free Future with Large Language Modelscited by 53
  7. 2022TEMPERA: Test-Time Prompting via Reinforcement Learningcited by 50
  8. 2022PERFECT: Prompt-free and Efficient Few-shot Learning with Language Modelscited by 40
  9. 2021Paradigm Shift in Natural Language Processingcited by 39
  10. 2021Good Examples Make A Faster Learner: Simple Demonstration-based Learning for Low-resource NERcited by 38
  11. 2021True Few-Shot Learning with Prompts -- A Real-World Perspectivecited by 38
  12. 2022Decoupling Knowledge from Memorization: Retrieval-augmented Prompt Learningcited by 36
  13. 2022Dynamic Prefix-Tuning for Generative Template-based Event Extractioncited by 35
  14. 2022Frustratingly Easy Label Projection for Cross-lingual Transfercited by 33
  15. 2021Few-Shot Cross-Lingual Stance Detection with Sentiment-Based Pre-Trainingcited by 28
  16. 2021LightNER: A Lightweight Tuning Paradigm for Low-resource NER via Pluggable Promptingcited by 27
  17. 2021Avoiding Inference Heuristics in Few-shot Prompt-based Finetuningcited by 23
  18. 2021Solving Aspect Category Sentiment Analysis as a Text Generation Taskcited by 21
  19. 2023A Comprehensive Survey on Relation Extraction: Recent Advances and New Frontierscited by 21
  20. 2022MetaPrompting: Learning to Learn Better Promptscited by 19
  21. 2024ReLiK: Retrieve and LinK, Fast and Accurate Entity Linking and Relation Extraction on an Academic Budgetcited by 16
  22. 2022PromptAttack: Prompt-based Attack for Language Models via Gradient Searchcited by 15
  23. 2023Effective Structured Prompting by Meta-Learning and Representative Verbalizercited by 14
  24. 2022Relation Extraction as Open-book Examination: Retrieval-enhanced Prompt Tuningcited by 13
  25. 2022Generative Prompt Tuning for Relation Classificationcited by 13
  26. 2024SpeechPrompt: Prompting Speech Language Models for Speech Processing Taskscited by 12
  27. 2022Prompt-Learning for Short Text Classificationcited by 11
  28. 2023Hierarchical Verbalizer for Few-Shot Hierarchical Text Classificationcited by 11
  29. 2023Scalable Prompt Generation for Semi-supervised Learning with Language Modelscited by 10
  30. 2021Semi-Supervised Exaggeration Detection of Health Science Press Releasescited by 8
  31. 2022Automatic Multi-Label Prompting: Simple and Interpretable Few-Shot Classificationcited by 8
  32. 2022Zero-shot Domain Adaptation for Neural Machine Translation with Retrieved Phrase-level Promptscited by 7
  33. 2023Is Prompt-Based Finetuning Always Better than Vanilla Finetuning? Insights from Cross-Lingual Language Understandingcited by 7
  34. 2023Open-vocabulary Video Question Answering: A New Benchmark for Evaluating the Generalizability of Video Question Answering Modelscited by 7
  35. 2023LMSanitator: Defending Prompt-Tuning Against Task-Agnostic Backdoorscited by 7
  36. 2022Eliciting Knowledge from Pretrained Language Models for Prototypical Prompt Verbalizercited by 6
  37. 2022Decoder Tuning: Efficient Language Understanding as Decodingcited by 6
  38. 2023PIEClass: Weakly-Supervised Text Classification with Prompting and Noise-Robust Iterative Ensemble Trainingcited by 6

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