mlsanityData to 14 Oct 2026

PRBoost: Prompt-Based Rule Discovery and Boosting for Interactive Weakly-Supervised Learning

arXiv 2203.09735 ↗First posted 18 Mar 2022cs.CL cs.LGcited by 14

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Abstract (arXiv)

Weakly-supervised learning (WSL) has shown promising results in addressing label scarcity on many NLP tasks, but manually designing a comprehensive, high-quality labeling rule set is tedious and difficult. We study interactive weakly-supervised learning -- the problem of iteratively and automatically discovering novel labeling rules from data to improve the WSL model. Our proposed model, named PRBoost, achieves this goal via iterative prompt-based rule discovery and model boosting. It uses boosting to identify large-error instances and then discovers candidate rules from them by prompting pre-trained LMs with rule templates. The candidate rules are judged by human experts, and the accepted rules are used to generate complementary weak labels and strengthen the current model. Experiments on four tasks show PRBoost outperforms state-of-the-art WSL baselines up to 7.1% and bridges the gaps with fully supervised models. Our Implementation is available at urlhttps://github.com/rz-zhang/PRBoost.

Key facts. Cited by 14 papers in the map (data through 14 Oct 2026). Builds on 38 of its 55 references that are in the map. First posted on arXiv 18 Mar 2022.

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38 of 55 references.

References · 38

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  3. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
  4. 2021Prefix-Tuning: Optimizing Continuous Prompts for Generationcited by 3,915
  5. 2021The Power of Scale for Parameter-Efficient Prompt Tuningcited by 3,795
  6. 2015Character-level Convolutional Networks for Text Classificationcited by 2,330
  7. 2021Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processingcited by 2,234
  8. 2019BioBERT: a pre-trained biomedical language representation model for biomedical text miningcited by 1,947
  9. 2020Making Pre-trained Language Models Better Few-shot Learnerscited by 1,256
  10. 2020Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inferencecited by 938
  11. 2015Unsupervised Deep Embedding for Clustering Analysiscited by 767
  12. 2021GPT Understands, Toocited by 716
26 more
  1. 2021P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Taskscited by 689
  2. 2020It's Not Just Size That Matters: Small Language Models Are Also Few-Shot Learnerscited by 669
  3. 2017Snorkel: Rapid Training Data Creation with Weak Supervisioncited by 372
  4. 2021True Few-Shot Learning with Language Modelscited by 344
  5. 2021PTR: Prompt Tuning with Rules for Text Classificationcited by 238
  6. 2021Knowledgeable Prompt-tuning: Incorporating Knowledge into Prompt Verbalizer for Text Classificationcited by 183
  7. 2021Active Learning by Acquiring Contrastive Examplescited by 156
  8. 2016Harnessing Deep Neural Networks with Logic Rulescited by 135
  9. 2020GSum: A General Framework for Guided Neural Abstractive Summarizationcited by 129
  10. 2017Deep Active Learning for Named Entity Recognitioncited by 108
  11. 2021WRENCH: A Comprehensive Benchmark for Weak Supervisioncited by 96
  12. 2020BOND: BERT-Assisted Open-Domain Named Entity Recognition with Distant Supervisioncited by 91
  13. 2020Fine-Tuning Pre-trained Language Model with Weak Supervision: A Contrastive-Regularized Self-Training Approachcited by 83
  14. 2022A Survey on Programmatic Weak Supervisioncited by 66
  15. 2020Learning from Rules Generalizing Labeled Exemplarscited by 59
  16. 2018Training Classifiers with Natural Language Explanationscited by 58
  17. 2020SeqMix: Augmenting Active Sequence Labeling via Sequence Mixupcited by 52
  18. 2020Interactive Weak Supervision: Learning Useful Heuristics for Data Labelingcited by 52
  19. 2020Named Entity Recognition without Labelled Data: A Weak Supervision Approachcited by 46
  20. 2019NERO: A Neural Rule Grounding Framework for Label-Efficient Relation Extractioncited by 24
  21. 2021Weakly Supervised Named Entity Tagging with Learnable Logical Rulescited by 21
  22. 2020Adaptive Rule Discovery for Labeling Text Datacited by 15
  23. 2021BERTifying the Hidden Markov Model for Multi-Source Weakly Supervised Named Entity Recognitioncited by 15
  24. 2021GLaRA: Graph-based Labeling Rule Augmentation for Weakly Supervised Named Entity Recognitioncited by 12
  25. 2022Nemo: Guiding and Contextualizing Weak Supervision for Interactive Data Programmingcited by 10
  26. 2021TagRuler: Interactive Tool for Span-Level Data Programming by Demonstrationcited by 4

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Citing papers per year: 2022: 5; 2023: 3; 2024: 5; 2025: 1.

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  1. 2022In-context Examples Selection for Machine Translationcited by 172
  2. 2022A Survey of Active Learning for Natural Language Processingcited by 40
  3. 2023ReGen: Zero-Shot Text Classification via Training Data Generation with Progressive Dense Retrievalcited by 26
  4. 2022A general-purpose material property data extraction pipeline from large polymer corpora using Natural Language Processingcited by 22
  5. 2022Cold-Start Data Selection for Few-shot Language Model Fine-tuning: A Prompt-Based Uncertainty Propagation Approachcited by 16
  6. 2023On the Limitations of Simulating Active Learningcited by 9
  7. 2022AutoWS-Bench-101: Benchmarking Automated Weak Supervision with 100 Labelscited by 7
  8. 2024TPD: Enhancing Student Language Model Reasoning via Principle Discovery and Guidancecited by 6
  9. 2025Calibrating Pre-trained Language Classifiers on LLM-generated Noisy Labels via Iterative Refinementcited by 4
  10. 2024RulePrompt: Weakly Supervised Text Classification with Prompting PLMs and Self-Iterative Logical Rulescited by 2
  11. 2024Interactive Machine Teaching by Labeling Rules and Instancescited by 2
  12. 2024Leveraging Large Language Models for Structure Learning in Prompted Weak Supervisioncited by 1
2 more
  1. 2023Local Boosting for Weakly-Supervised Learningcited by 0
  2. 2024Towards Enhancing Linked Data Retrieval in Conversational UIs using Large Language Modelscited by 0