AI RESEARCH
Probing the Knowledge Boundary: An Interactive Agentic Framework for Deep Knowledge Extraction
arXiv CS.LG
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ArXi:2602.00959v2 Announce Type: replace Large Language Models (LLMs) can be seen as compressed knowledge bases, but it remains unclear what knowledge they truly contain and how far their knowledge boundary extends. Existing benchmarks are mostly static and provide limited for systematic knowledge probing. In this paper, we propose an interactive agentic framework to systematically extract and quantify the knowledge of LLMs. Our method includes four adaptive exploration policies to probe knowledge at different granularity. To ensure the quality of extracted knowledge, we.