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About this paper
Shiyu Ni, Keping Bi, Jiafeng Guo, Lulu Yu, Baolong Bi, Xueqi Cheng
Researchers from the CAS Key Lab of Network Data Science and Technology and the University of Chinese Academy of Sciences explored leveraging Large Language Model (LLM) internal states to enhance their ability to perceive knowledge boundaries. The work introduces methods for pre-generation confidence estimation to improve efficiency and a Confidence Consistency-based Calibration (C3) strategy, which increased the Unknown Perception Rate by up to 5.6% on NQ and 4.9% on HotpotQA by effectively reducing overconfidence.
Key insights
- LLMs exhibit a significant ability to perceive their knowledge boundaries *before* generating a full response, offering a mechanism for early exits and efficiency gains during inference.
- Incorporating internal states from the *generated answer* consistently refines and enhances an LLM's overall confidence perception compared to pre-generation states, demonstrating an information gain during the generation process.
- The Confidence Consistency-based Calibration (C3) method effectively mitigates LLM overconfidence by leveraging self-generated reformulations, leading to a substantial increase in the Unknown Perception Rate.
Summary by alphaXiv.
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