mlsanityData to 14 Oct 2026

Refining Labeling Functions with Limited Labeled Data

arXiv 2505.23470 ↗First posted 29 May 2025cs.LG cs.IT math.ITcited by 1

Not on the map. Why?

Abstract (arXiv)

Programmatic weak supervision (PWS) significantly reduces human effort for labeling data by combining the outputs of user-provided labeling functions (LFs) on unlabeled datapoints. However, the quality of the generated labels depends directly on the accuracy of the LFs. In this work, we study the problem of fixing LFs based on a small set of labeled examples. Towards this goal, we develop novel techniques for repairing a set of LFs by minimally changing their results on the labeled examples such that the fixed LFs ensure that (i) there is sufficient evidence for the correct label of each labeled datapoint and (ii) the accuracy of each repaired LF is sufficiently high. We model LFs as conditional rules which enables us to refine them, i.e., to selectively change their output for some inputs. We demonstrate experimentally that our system improves the quality of LFs based on surprisingly small sets of labeled datapoints.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 22 of its 44 references that are in the map. First posted on arXiv 29 May 2025.

Builds on

22 of 44 references.

References · 22

  1. 2015Character-level Convolutional Networks for Text Classificationcited by 2,330
  2. 2016Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filteringcited by 520
  3. 2019Confident Learning: Estimating Uncertainty in Dataset Labelscited by 413
  4. 2017Snorkel: Rapid Training Data Creation with Weak Supervisioncited by 372
  5. 2019Bias in Bios: A Case Study of Semantic Representation Bias in a High-Stakes Settingcited by 349
  6. 2016Data Programming: Creating Large Training Sets, Quicklycited by 246
  7. 2023Language Models Enable Simple Systems for Generating Structured Views of Heterogeneous Data Lakescited by 111
  8. 2020Fast and Three-rious: Speeding Up Weak Supervision with Triplet Methodscited by 98
  9. 2021WRENCH: A Comprehensive Benchmark for Weak Supervisioncited by 96
  10. 2020Fine-Tuning Pre-trained Language Model with Weak Supervision: A Contrastive-Regularized Self-Training Approachcited by 83
  11. 2017HoloClean: Holistic Data Repairs with Probabilistic Inferencecited by 79
  12. 2022A Survey on Programmatic Weak Supervisioncited by 66
10 more
  1. 2019CrossWeigh: Training Named Entity Tagger from Imperfect Annotationscited by 60
  2. 2018Training Classifiers with Natural Language Explanationscited by 58
  3. 2020Interactive Weak Supervision: Learning Useful Heuristics for Data Labelingcited by 52
  4. 2020Adaptive Rule Discovery for Labeling Text Datacited by 15
  5. 2022Nemo: Guiding and Contextualizing Weak Supervision for Interactive Data Programmingcited by 10
  6. 2022Understanding Programmatic Weak Supervision via Source-aware Influence Functioncited by 8
  7. 2023Alfred: A System for Prompted Weak Supervisioncited by 8
  8. 2024LLM-assisted Labeling Function Generation for Semantic Type Detectioncited by 2
  9. 2022ULF: Unsupervised Labeling Function Correction using Cross-Validation for Weak Supervisioncited by 1
  10. 2023Optimising Human-Machine Collaboration for Efficient High-Precision Information Extraction from Text Documentscited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Structured Exploration and Exploitation of Label Functions for Automated Data Annotationcited by 1