Papers by Yeyang Zhou
Entropy-based Exploration Conduction for Multi-step Reasoning (2025.findings-acl)
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| Challenge: | Existing methods to automatically decide the depth of exploration of the reasoning procedure lead to high cost and a lack of flexibility. |
| Approach: | They propose a method that dynamically adjusts the exploration depth during multi-step reasoning by monitoring LLM’s output entropy and variance entropic. |
| Outcome: | The proposed method captures the uncertainty of the current step and the fluctuation of uncertainty across consecutive reasoning steps and then selects whether to deepen, expand, or stop exploration according to the probability. |