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About this paper
Zhenjiang Mao, Anirudhh Venkat
Researchers from the University of Florida developed the Recurrent Confidence Chain (RCC) framework for Large Language Models, which quantifies uncertainty by considering temporal dynamics and semantic correlations within multi-step reasoning. The framework demonstrates significantly improved calibration, with lower Expected Calibration Error (ECE), and competitive predictive quality (NLL) on complex reasoning benchmarks like GAOKAO Math and CLadder, enhancing the trustworthiness of LLM outputs.
Key insights
- Explicitly modeling semantic correlations between consecutive reasoning steps via an inter-step attention mechanism improves the accuracy of uncertainty assessment.
- An efficient, linear-complexity recurrent mechanism can effectively accumulate and propagate historical confidence across long reasoning chains.
- Optimal uncertainty quantification requires balancing the influence of current step confidence with the accumulated historical confidence, controlled by a propagation weight (δ).
Summary by alphaXiv.
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