A Two-Stage LLM Framework for Accessible and Verified XAI Explanations

Researchers have developed a Two-Stage LLM Meta-Verification Framework to enhance eXplainable Artificial Intelligence (XAI) by ensuring accurate and reliable natural-language explanations through an iterative refinement process involving both an Explainer and a Verifier LLM. This framework is crucial for developers as it addresses the current limitations of subjective evaluation methods, offering safeguards against flawed XAI explanations reaching end-users while improving linguistic clarity and coherence.

Read the full article at arXiv cs.AI (Artificial Intelligence)

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