Tag: AI explainability
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Efficient Proxy Models for Interpreting Large Language Models
Discover a budget-friendly proxy framework that boosts LLM interpretability with 90% fidelity and 89% cost savings for scalable AI optimization.
Source-Faithful Explanation Planning in Retrieval-Augmented LLMs
Enhance source-faithful explanations in retrieval-augmented language models using illocutionary planning and chain-of-illocution prompting for better relia...
AI Governance Control Stack for Stable, Accountable AI
Discover the AI Governance Control Stack that ensures operational stability, transparency, and accountability in AI systems for enterprise reliability.
Feature Attribution Stability Suite: Evaluating Post-Hoc Methods
Discover how the Feature Attribution Stability Suite benchmarks post-hoc attribution methods for stability across perturbations with prediction-invariance...
Minimum Spanning Tree Compactness for Better Explanation Quality
Discover MST-C, a new metric enhancing explanation quality by measuring structural compactness and improving attribution clarity in AI models.