LLM-FACETS: A Privacy-Preserving Framework for Evaluating LLM Transparency and Accountability
Researchers have developed LLM-FACETS, an open-source framework designed to make evaluating Large Language Models more accessible and privacy-preserving. The system features a browser-accessible interface and a plugin architecture tailored for technical experts, domain experts, and compliance officers, aligning with frameworks like the EU AI Act. LLM-FACETS operationalizes transparency by visualizing log-probabilities for uncertainty, using multi-judge consensus, and employing RAG Triad metrics to detect hallucinations, all while ensuring data remains within a self-hosted server. AI
IMPACT Enhances AI auditing capabilities for non-technical users, improving transparency and accountability in LLM deployment.