mlsanityData to 7 Oct 2026

INTERACTION: A Generative XAI Framework for Natural Language Inference Explanations

arXiv 2209.01061 ↗First posted 2 Sep 2022cs.CL cs.AIcited by 5

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Abstract (arXiv)

XAI with natural language processing aims to produce human-readable explanations as evidence for AI decision-making, which addresses explainability and transparency. However, from an HCI perspective, the current approaches only focus on delivering a single explanation, which fails to account for the diversity of human thoughts and experiences in language. This paper thus addresses this gap, by proposing a generative XAI framework, INTERACTION (explaIn aNd predicT thEn queRy with contextuAl CondiTional varIational autO-eNcoder). Our novel framework presents explanation in two steps: (step one) Explanation and Label Prediction; and (step two) Diverse Evidence Generation. We conduct intensive experiments with the Transformer architecture on a benchmark dataset, e-SNLI. Our method achieves competitive or better performance against state-of-the-art baseline models on explanation generation (up to 4.7% gain in BLEU) and prediction (up to 4.4% gain in accuracy) in step one; it can also generate multiple diverse explanations in step two.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 36 of its 55 references that are in the map. First posted on arXiv 2 Sep 2022.

Builds on

36 of 55 references.

References · 36

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24 more
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Cited by year

Citing papers per year: 2022: 1; 2023: 0; 2024: 2; 2025: 2.

5 papers.

  1. 2025Towards Transparent AI: A Survey on Explainable Large Language Modelscited by 7
  2. 2025LangVAE and LangSpace: Building and Probing for Language Model VAEscited by 3
  3. 2024Through the Thicket: A Study of Number-Oriented LLMs derived from Random Forest Modelscited by 2
  4. 2024Enhancing adversarial robustness in Natural Language Inference using explanationscited by 2
  5. 2022Incorporating Emotions into Health Mention Classification Task on Social Mediacited by 0