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

Researchers propose a two-stage LLM framework that uses one model to translate XAI technical outputs into natural language and a second model to verify accuracy, faithfulness, and completeness before delivering explanations to users. The framework includes iterative refinement mechanisms and demonstrates improved reliability across multiple XAI techniques and LLM families.

This research addresses a critical gap in AI transparency infrastructure: the challenge of making machine learning decisions understandable to non-technical users without sacrificing accuracy. While LLMs excel at generating fluent explanations, they often hallucinate or misrepresent underlying technical details. The proposed two-stage verification framework introduces a novel safeguard by deploying a dedicated verification model that acts as quality control, checking explanations against criteria like faithfulness and coherence before they reach end-users.

The work builds on growing recognition that explainability requires more than post-hoc analysis. Current XAI translation methods lack formal verification mechanisms, creating risk for critical applications in healthcare, finance, and other high-stakes domains. This research positions verification as a foundational requirement rather than optional enhancement. The iterative refinement process, guided by Entropy Production Rate metrics, suggests the framework progressively improves explanation quality through structured feedback loops—a methodologically sound approach to increasing model stability.

For the broader AI industry, this framework addresses regulatory and ethical pressures around model interpretability. As oversight bodies increasingly demand explainable systems, tools that can reliably translate technical outputs into verified narratives become commercially valuable. For developers and researchers, the framework's effectiveness across multiple LLM families suggests practical applicability rather than vendor lock-in. The paper's emphasis on accessibility alongside accuracy reflects market demand for democratized AI systems that don't require specialized expertise to understand.

The next critical step involves testing this framework in production environments with diverse user populations and use cases. Real-world deployment will reveal whether verification mechanisms scale efficiently and whether the framework's benefits extend to explaining complex multi-model systems.

• →A two-stage LLM framework using an Explainer and Verifier model provides reliable, verified natural-language explanations of AI decisions.
• →The verification stage filters out hallucinations and faithfulness failures, significantly improving explanation quality over raw XAI outputs.
• →Iterative refinement mechanisms guided by feedback progressively increase explanation coherence and stability.
• →The framework demonstrates effectiveness across five XAI techniques and three families of open-weight LLMs, indicating broad applicability.
• →Verified explanation systems address regulatory demand for trustworthy, accessible AI while supporting democratization of AI interpretation.