AI RESEARCH
LLM-FACETS: A Privacy-Preserving Framework for Evaluating LLM Transparency and Accountability
arXiv CS.AI
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ArXi:2605.31167v1 Announce Type: new Assessing whether Large Language Models outputs are factually grounded, epistemically calibrated, and methodologically reproducible is a prerequisite for responsible AI deployment. Yet auditing LLMs remains inaccessible to non-technical practitioners: existing tools require programming expertise and non-trivial environment setup, and cloud-hosted platforms transmit evaluation data to external services, creating barriers for domain experts and compliance officers legally responsible for AI oversight. We.