mlsanityData to 7 Oct 2026

e-SNLI: Natural Language Inference with Natural Language Explanations

arXiv 1812.01193 ↗First posted 4 Dec 2018cs.CLcited by 468

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Cited by 67 in LLM reasoning, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

In order for machine learning to garner widespread public adoption, models must be able to provide interpretable and robust explanations for their decisions, as well as learn from human-provided explanations at train time. In this work, we extend the Stanford Natural Language Inference dataset with an additional layer of human-annotated natural language explanations of the entailment relations. We further implement models that incorporate these explanations into their training process and output them at test time. We show how our corpus of explanations, which we call e-SNLI, can be used for various goals, such as obtaining full sentence justifications of a model's decisions, improving universal sentence representations and transferring to out-of-domain NLI datasets. Our dataset thus opens up a range of research directions for using natural language explanations, both for improving models and for asserting their trust.

Key facts. Cited by 468 papers in the map (data through 7 Oct 2026). First posted on arXiv 4 Dec 2018.

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Citing papers per year: 2021: 61; 2022: 96; 2023: 107; 2024: 88; 2025: 75; 2026: 41.

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  1. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  2. 2022Self-Consistency Improves Chain of Thought Reasoning in Language Modelscited by 5,601
  3. 2022Scaling Instruction-Finetuned Language Modelscited by 2,997
  4. 2021Scaling Language Models: Methods, Analysis & Insights from Training Gophercited by 1,199
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  6. 2023Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizescited by 710
  7. 2022Large Language Models Can Self-Improvecited by 656
  8. 2023An Empirical Study of Catastrophic Forgetting in Large Language Models During Continual Fine-tuningcited by 622
  9. 2021Recent Advances in Natural Language Processing via Large Pre-Trained Language Models: A Surveycited by 457
  10. 2021Measuring and Improving Consistency in Pretrained Language Modelscited by 361
  11. 2022From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AIcited by 299
  12. 2022Can language models learn from explanations in context?cited by 248
38 more
  1. 2024LLMs instead of Human Judges? A Large Scale Empirical Study across 20 NLP Evaluation Taskscited by 236
  2. 2022OPT-IML: Scaling Language Model Instruction Meta Learning through the Lens of Generalizationcited by 235
  3. 2021Get Your Vitamin C! Robust Fact Verification with Contrastive Evidencecited by 222
  4. 2022On Second Thought, Let's Not Think Step by Step! Bias and Toxicity in Zero-Shot Reasoningcited by 191
  5. 2022ROSCOE: A Suite of Metrics for Scoring Step-by-Step Reasoningcited by 175
  6. 2022The Unreliability of Explanations in Few-shot Prompting for Textual Reasoningcited by 169
  7. 2023Is ChatGPT better than Human Annotators? Potential and Limitations of ChatGPT in Explaining Implicit Hate Speechcited by 165
  8. 2023Clever Hans or Neural Theory of Mind? Stress Testing Social Reasoning in Large Language Modelscited by 161
  9. 2022Maieutic Prompting: Logically Consistent Reasoning with Recursive Explanationscited by 159
  10. 2022A Review on Language Models as Knowledge Basescited by 156
  11. 2021Post-hoc Interpretability for Neural NLP: A Surveycited by 155
  12. 2022Fine-tuned Language Models are Continual Learnerscited by 152
  13. 2021Explaining Answers with Entailment Treescited by 148
  14. 2023Symbolic Chain-of-Thought Distillation: Small Models Can Also "Think" Step-by-Stepcited by 144
  15. 2022Natural Language Descriptions of Deep Visual Featurescited by 136
  16. 2022Language Models as Agent Modelscited by 131
  17. 2022Towards Faithful Model Explanation in NLP: A Surveycited by 129
  18. 2021Teach Me to Explain: A Review of Datasets for Explainable Natural Language Processingcited by 126
  19. 2022Toward Transparent AI: A Survey on Interpreting the Inner Structures of Deep Neural Networkscited by 126
  20. 2021Reframing Human-AI Collaboration for Generating Free-Text Explanationscited by 125
  21. 2023Training Language Models with Language Feedback at Scalecited by 125
  22. 2022Explanations from Large Language Models Make Small Reasoners Bettercited by 123
  23. 2023Automatic Prompt Augmentation and Selection with Chain-of-Thought from Labeled Datacited by 119
  24. 2022Rationale-Augmented Ensembles in Language Modelscited by 111
  25. 2022Fusing finetuned models for better pretrainingcited by 110
  26. 2024Rethinking Interpretability in the Era of Large Language Modelscited by 110
  27. 2024Making Reasoning Matter: Measuring and Improving Faithfulness of Chain-of-Thought Reasoningcited by 108
  28. 2023Faithfulness Tests for Natural Language Explanationscited by 107
  29. 2023The CoT Collection: Improving Zero-shot and Few-shot Learning of Language Models via Chain-of-Thought Fine-Tuningcited by 106
  30. 2023SCOTT: Self-Consistent Chain-of-Thought Distillationcited by 103
  31. 2024Are self-explanations from Large Language Models faithful?cited by 101
  32. 2023Shepherd: A Critic for Language Model Generationcited by 96
  33. 2023Can Large Language Models Explain Themselves? A Study of LLM-Generated Self-Explanationscited by 90
  34. 2023Do Models Explain Themselves? Counterfactual Simulatability of Natural Language Explanationscited by 85
  35. 2021e-ViL: A Dataset and Benchmark for Natural Language Explanations in Vision-Language Taskscited by 84
  36. 2021Few-Shot Self-Rationalization with Natural Language Promptscited by 83
  37. 2021Explainability of deep vision-based autonomous driving systems: Review and challengescited by 80
  38. 2023Exploring the Benefits of Training Expert Language Models over Instruction Tuningcited by 80

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