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

1. 1Build a two-stage LLM framework using a large language model and a verification module to generate explanations for XAI methods
2. 2Train the LLM on a dataset of technical outputs from XAI methods and corresponding natural-language explanations
3. 3Evaluate the accuracy, faithfulness, and completeness of the generated explanations using quantitative metrics
4. 4Fine-tune the LLM and verification module to improve the quality of the explanations
5. 5Integrate the two-stage framework into an existing XAI pipeline to provide accessible and verified explanations for AI-driven decisions

Data scientists and AI engineers can benefit from this framework to improve the explainability of their models, while product managers can use it to increase user trust in AI-driven products

💡 A two-stage LLM framework can provide accurate, faithful, and complete explanations for XAI methods, increasing user trust in AI-driven products

🚀 Enhance trust in AI decision-making with a two-stage LLM framework for accessible and verified XAI explanations! #XAI #LLM #AI