# Enhancing XAI Narratives through Multi-Narrative Refinement and Knowledge Distillation

arXiv: https://arxiv.org/abs/2510.03134 · HTML: https://mlsanity.com/paper/2510.03134/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Knowledge distillation for LLMs, one of 12 directions in Knowledge distillation.
- Builds on 11 of its 29 references that are in the map.
- First posted on arXiv 3 Oct 2025.

## Placed in

- Knowledge distillation / [Knowledge distillation for LLMs](https://mlsanity.com/distill/knowledge-distillation-for-llms/)

## Builds on (most cited first, 11 of 11)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Explaining Machine Learning Classifiers through Diverse Counterfactual Explanations](https://mlsanity.com/paper/1905.07697/) (2019)
- [AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors](https://mlsanity.com/paper/2308.10848/) (2023)
- [Small Language Models Need Strong Verifiers to Self-Correct Reasoning](https://mlsanity.com/paper/2404.17140/) (2024)
- [LLMs for Generating and Evaluating Counterfactuals: A Comprehensive Study](https://mlsanity.com/paper/2405.00722/) (2024)
- [Self-Refine Instruction-Tuning for Aligning Reasoning in Language Models](https://mlsanity.com/paper/2405.00402/) (2024)
- [Tell Me a Story! Narrative-Driven XAI with Large Language Models](https://mlsanity.com/paper/2309.17057/) (2023)
- [Small Language Models Improve Giants by Rewriting Their Outputs](https://mlsanity.com/paper/2305.13514/) (2023)
- [GraphXAIN: Narratives to Explain Graph Neural Networks](https://mlsanity.com/paper/2411.02540/) (2024)
- [How good is my story? Towards quantitative metrics for evaluating LLM-generated XAI narratives](https://mlsanity.com/paper/2412.10220/) (2024)
- [Using LLMs for Explaining Sets of Counterfactual Examples to Final Users](https://mlsanity.com/paper/2408.15133/) (2024)

## Built on by (most cited first, 2 of 2)

- [An Agentic Approach to Generating XAI-Narratives](https://mlsanity.com/paper/2603.20003/) (2026)
- [PONTE: Personalized Orchestration for Natural Language Trustworthy Explanations](https://mlsanity.com/paper/2603.06485/) (2026)

## Abstract (arXiv)

Explainable Artificial Intelligence has become a crucial area of research, aiming to demystify the decision-making processes of deep learning models. Among various explainability techniques, counterfactual explanations have been proven particularly promising, as they offer insights into model behavior by highlighting minimal changes that would alter a prediction. Despite their potential, these explanations are often complex and technical, making them difficult for non-experts to interpret. To address this challenge, we propose a novel pipeline that leverages Language Models, large and small, to compose narratives for counterfactual explanations. We employ knowledge distillation techniques along with a refining mechanism to enable Small Language Models to perform comparably to their larger counterparts while maintaining robust reasoning abilities. In addition, we introduce a simple but effective evaluation method to assess natural language narratives, designed to verify whether the models' responses are in line with the factual, counterfactual ground truth. As a result, our proposed pipeline enhances both the reasoning capabilities and practical performance of student models, making them more suitable for real-world use cases.