Enhancing XAI Narratives through Multi-Narrative Refinement and Knowledge Distillation
This work addresses the challenge of making AI explanations accessible to non-experts, though it appears incremental in improving existing methods.
The paper tackled the problem of complex and technical counterfactual explanations in XAI by proposing a pipeline using language models and knowledge distillation to generate narratives, resulting in enhanced reasoning and performance for student models suitable for real-world use.
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.