AI RESEARCH
From XAI to Stories: A Factorial Study of LLM-Generated Explanation Quality
arXiv CS.CL
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ArXi:2601.02224v2 Announce Type: replace Explainable AI (XAI) methods like SHAP and LIME produce numerical feature attributions that remain inaccessible to non expert users. Prior work has shown that Large Language Models (LLMs) can transform these outputs into natural language explanations (NLEs), but it remains unclear which factors contribute to high-quality explanations. We present a systematic factorial study investigating how Forecasting model choice, XAI method, LLM selection, and prompting strategy affect NLE quality.