mlsanityData to 14 Oct 2026

Investigating the Effect of Natural Language Explanations on Out-of-Distribution Generalization in Few-shot NLI

arXiv 2110.06223 ↗First posted 12 Oct 2021cs.CL cs.AIcited by 7

Not on the map. Why?

Abstract (arXiv)

Although neural models have shown strong performance in datasets such as SNLI, they lack the ability to generalize out-of-distribution (OOD). In this work, we formulate a few-shot learning setup and examine the effects of natural language explanations on OOD generalization. We leverage the templates in the HANS dataset and construct templated natural language explanations for each template. Although generated explanations show competitive BLEU scores against groundtruth explanations, they fail to improve prediction performance. We further show that generated explanations often hallucinate information and miss key elements that indicate the label.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 11 of its 18 references that are in the map. First posted on arXiv 12 Oct 2021.

Builds on

11 of 18 references.

References · 11

  1. 2017Attention Is All You Needcited by 60,361
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  3. 2019Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural Language Inferencecited by 876
  4. 2018e-SNLI: Natural Language Inference with Natural Language Explanationscited by 468
  5. 2020Dataset Cartography: Mapping and Diagnosing Datasets with Training Dynamicscited by 425
  6. 2019Explain Yourself! Leveraging Language Models for Commonsense Reasoningcited by 367
  7. 2019Don't Take the Easy Way Out: Ensemble Based Methods for Avoiding Known Dataset Biasescited by 280
  8. 2020Adversarial Filters of Dataset Biasescited by 160
  9. 2020WT5?! Training Text-to-Text Models to Explain their Predictionscited by 148
  10. 2020Conditionally Adaptive Multi-Task Learning: Improving Transfer Learning in NLP Using Fewer Parameters & Less Datacited by 50
  11. 2019Towards Realistic Practices In Low-Resource Natural Language Processing: The Development Setcited by 16

Built on by

Cited by year

Citing papers per year: 2022: 2; 2023: 2; 2024: 0; 2025: 3.

7 papers.

  1. 2022The Unreliability of Explanations in Few-shot Prompting for Textual Reasoningcited by 169
  2. 2022State-of-the-art generalisation research in NLP: A taxonomy and reviewcited by 98
  3. 2023Shall We Pretrain Autoregressive Language Models with Retrieval? A Comprehensive Studycited by 63
  4. 2023FLamE: Few-shot Learning from Natural Language Explanationscited by 10
  5. 2025Self-Rationalization in the Wild: A Large Scale Out-of-Distribution Evaluation on NLI-related taskscited by 1
  6. 2025Bayesian Cross-Modal Alignment Learning for Few-Shot Out-of-Distribution Generalizationcited by 1
  7. 2025Do Generalisation Results Generalise?cited by 0