Probing the Knowledge Boundary: An Interactive Agentic Framework for Deep Knowledge Extraction
arXiv:2602.00959v2 Announce Type: replace Abstract: 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 support 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 int
The rapid advancement and widespread deployment of Large Language Models necessitate methods for understanding their internal knowledge and limitations to improve their reliability and safety.
Understanding the 'knowledge boundary' of LLMs is critical for developing more robust, verifiable AI systems and for effectively leveraging their capabilities without overreliance or misinterpretation.
This framework introduces a structured, interactive approach to systematically extract and quantify LLM knowledge, moving beyond static benchmarks to dynamic probing.
- · AI researchers
- · Developers of AI applications
- · Enterprises reliant on LLMs
- · Undifferentiated LLM providers
- · Legacy AI evaluation methods
Improved understanding and interpretability of large language models, leading to more targeted fine-tuning and application development.
Development of new LLM architectures specifically designed for better knowledge extraction and explainability, potentially leading to 'glass-box' AI.
Enhanced AI safety and auditability, accelerating regulatory frameworks and public trust in advanced AI systems.
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Read at arXiv cs.LG