Current methods using LLMs to translate technical XAI outputs into natural language often lack guarantees of accuracy and completeness. This paper introduces a Two-Stage LLM Meta-Verification Framework that employs an Explainer LLM for generating explanations and a Verifier LLM to assess and refine them iteratively, significantly enhancing the trustworthiness and accessibility of XAI.