mlsanityData to 14 Oct 2026

Training Classifiers with Natural Language Explanations

arXiv 1805.03818 ↗First posted 10 May 2018cs.CLcited by 58

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Abstract (arXiv)

Training accurate classifiers requires many labels, but each label provides only limited information (one bit for binary classification). In this work, we propose BabbleLabble, a framework for training classifiers in which an annotator provides a natural language explanation for each labeling decision. A semantic parser converts these explanations into programmatic labeling functions that generate noisy labels for an arbitrary amount of unlabeled data, which is used to train a classifier. On three relation extraction tasks, we find that users are able to train classifiers with comparable F1 scores from 5-100× faster by providing explanations instead of just labels. Furthermore, given the inherent imperfection of labeling functions, we find that a simple rule-based semantic parser suffices.

Key facts. Cited by 58 papers in the map (data through 14 Oct 2026). First posted on arXiv 10 May 2018.

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Citing papers per year: 2020: 1; 2021: 19; 2022: 18; 2023: 10; 2024: 6; 2025: 2; 2026: 2.

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  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  2. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,459
  3. 2023DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelinescited by 832
  4. 2022The Unreliability of Explanations in Few-shot Prompting for Textual Reasoningcited by 169
  5. 2021Explaining Answers with Entailment Treescited by 152
  6. 2021Teach Me to Explain: A Review of Datasets for Explainable Natural Language Processingcited by 126
  7. 2021Few-Shot Self-Rationalization with Natural Language Promptscited by 83
  8. 2022Learning and Evaluating Graph Neural Network Explanations based on Counterfactual and Factual Reasoningcited by 79
  9. 2021When Can Models Learn From Explanations? A Formal Framework for Understanding the Roles of Explanation Datacited by 75
  10. 2023Learning to Generate Better Than Your LLMcited by 49
  11. 2022e-CARE: a New Dataset for Exploring Explainable Causal Reasoningcited by 47
  12. 2020Learning Rewards from Linguistic Feedbackcited by 41
38 more
  1. 2021Multi-Vector Models with Textual Guidance for Fine-Grained Scientific Document Similaritycited by 35
  2. 2022Fixing Model Bugs with Natural Language Patchescited by 33
  3. 2021Local Interpretations for Explainable Natural Language Processing: A Surveycited by 31
  4. 2023Large Language Model Instruction Following: A Survey of Progresses and Challengescited by 31
  5. 2021On the Diversity and Limits of Human Explanationscited by 30
  6. 2021Refining Language Models with Compositional Explanationscited by 27
  7. 2021What to Learn, and How: Toward Effective Learning from Rationalescited by 25
  8. 2021Teaching Autoregressive Language Models Complex Tasks By Demonstrationcited by 15
  9. 2022A survey on improving NLP models with human explanationscited by 15
  10. 2022PRBoost: Prompt-Based Rule Discovery and Boosting for Interactive Weakly-Supervised Learningcited by 14
  11. 2022Factual and Informative Review Generation for Explainable Recommendationcited by 14
  12. 2021Micromodels for Efficient, Explainable, and Reusable Systems: A Case Study on Mental Healthcited by 13
  13. 2022Saliency Map Verbalization: Comparing Feature Importance Representations from Model-free and Instruction-based Methodscited by 13
  14. 2023FLamE: Few-shot Learning from Natural Language Explanationscited by 10
  15. 2021HERALD: An Annotation Efficient Method to Detect User Disengagement in Social Conversationscited by 9
  16. 2022CLUES: A Benchmark for Learning Classifiers using Natural Language Explanationscited by 9
  17. 2022Does Self-Rationalization Improve Robustness to Spurious Correlations?cited by 9
  18. 2024On Evaluating Explanation Utility for Human-AI Decision Making in NLPcited by 9
  19. 2022Why Is It Hate Speech? Masked Rationale Prediction for Explainable Hate Speech Detectioncited by 7
  20. 2024INTERACT: Enabling Interactive, Question-Driven Learning in Large Language Modelscited by 6
  21. 2022It Takes Two Flints to Make a Fire: Multitask Learning of Neural Relation and Explanation Classifierscited by 5
  22. 2023BYOC: Personalized Few-Shot Classification with Co-Authored Class Descriptionscited by 5
  23. 2021TagRuler: Interactive Tool for Span-Level Data Programming by Demonstrationcited by 4
  24. 2022Adaptive Multi-view Rule Discovery for Weakly-Supervised Compatible Products Predictioncited by 4
  25. 2022Zero-Shot Classification by Logical Reasoning on Natural Language Explanationscited by 4
  26. 2023Parallel Sentence-Level Explanation Generation for Real-World Low-Resource Scenarioscited by 4
  27. 2023Neglected Free Lunch -- Learning Image Classifiers Using Annotation Byproductscited by 4
  28. 2021D-REX: Dialogue Relation Extraction with Explanationscited by 3
  29. 2022SemSup: Semantic Supervision for Simple and Scalable Zero-shot Generalizationcited by 2
  30. 2022LaSQuE: Improved Zero-Shot Classification from Explanations Through Quantifier Modeling and Curriculum Learningcited by 2
  31. 2023Leveraging Multiple Teachers for Test-Time Adaptation of Language-Guided Classifierscited by 2
  32. 2023What if you said that differently?: How Explanation Formats Affect Human Feedback Efficacy and User Perceptioncited by 2
  33. 2021AutoTriggER: Label-Efficient and Robust Named Entity Recognition with Auxiliary Trigger Extractioncited by 1
  34. 2021Open Rule Inductioncited by 1
  35. 2022Label Augmentation with Reinforced Labeling for Weak Supervisioncited by 1
  36. 2024ActiveDP: Bridging Active Learning and Data Programmingcited by 1
  37. 2024WeShap: Weak Supervision Source Evaluation with Shapley Valuescited by 1
  38. 2024A look under the hood of the Interactive Deep Learning Enterprise (No-IDLE)cited by 1

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