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LLMs for XAI: Future Directions for Explaining Explanations
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In response to the demand for Explainable Artificial Intelligence (XAI), we investigate the use of Large Language Models (LLMs) to transform ML explanations into natural, human-readable narratives. Rather than directly explaining ML models using LLMs, we focus on refining explanations computed using existing XAI algorithms. We outline several research directions, including defining evaluation metrics, prompt design, comparing LLM models, exploring further training methods, and integrating external data. Initial experiments and user study suggest that LLMs offer a promising way to enhance the interpretability and usability of XAI.
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Cited by 1 Pith paper
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Decision Information Meets Large Language Models: The Future of Explainable Operations Research
An LLM framework that couples what-if analysis with graph edit distance on linear programs can generate more accurate and more detailed explanations for operations research queries than existing LLM baselines.
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