Towards Fully Exploiting LLM Internal States to Enhance Knowledge Boundary Perception

Understand

Large language models (LLMs) exhibit impressive performance across diverse tasks but often struggle to accurately gauge their knowledge boundaries, leading to confident yet incorrect responses.

• This paper explores leveraging LLMs' internal states to enhance their perception of knowledge boundaries from efficiency and risk perspectives.
• We investigate whether LLMs can estimate their confidence using internal states before response generation, potentially saving computational resources.
• Our experiments on datasets like Natural Questions, HotpotQA, and MMLU reveal that LLMs demonstrate significant pre-generation perception, which is further refined post-generation, with perception gaps remaining stable across varying conditions.