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arXiv 2502.00511cs.LGcs.AIcs.CL
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弥合内部概率与自一致性以实现高效且有效的大语言模型推理

Bridging Internal Probability and Self-Consistency for Effective and Efficient LLM Reasoning

National Key Laboratory for Novel Software Technology, Nanjing University（南京大学计算机软件新技术国家重点实验室）；School of Artificial Intelligence, Nanjing University（南京大学人工智能学院）；School of Intelligence Science and Technology, Nanjing University（南京大学智能科学与技术学院）

Zhi Zhou, Tan Yuhao, Zenan Li, Yuan Yao, Lan-Zhe Guo, Xiaoxing Ma, Yu-Feng Li

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Preliminary work
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AI总结：

本文通过理论误差分解分析揭示困惑度与自一致性方法的局限，提出推理剪枝困惑度一致性（RPC）方法，在七个基准数据集上显著提升LLM推理性能、样本效率和置信度可靠性。

摘要：

近年来，大语言模型（LLMs）的进步展现出卓越的推理能力。然而，单轮推理在复杂推理任务中往往产生不可靠的结果，促使研究者通过困惑度和自一致性等方法探索多条推理路径。本文首次对这些技术进行理论误差分解分析，将其误差拆分为估计误差和模型误差。分析揭示了一个基本权衡：困惑度方法因缺乏合适的一致性函数而存在显著模型误差，而自一致性因误差收敛速度慢而具有高估计误差。为克服这些局限，我们提出推理剪枝困惑度一致性（RPC）。该方法结合将LLM困惑度与自一致性无缝集成的困惑度一致性，以及消除低概率推理路径以有效防止估计误差降低退化的推理剪枝。理论分析表明，RPC不仅将估计误差的收敛速度提升至指数级，还具有进一步降低模型误差的巨大潜力。在七个基准数据集上的广泛实证评估证实，RPC能显著提升推理性能、样本效率和置信度可靠性。

英文摘要：

Recent advancements in large language models (LLMs) have demonstrated remarkable reasoning capabilities. However, single-shot inference often yields unreliable results for complex reasoning tasks, leading researchers to explore multiple reasoning paths through methods such as perplexity and self-consistency. In this paper, we present the first theoretical error decomposition analysis of these techniques, breaking down their error into estimation error and model error. Our analysis reveals a fundamental trade-off: perplexity methods suffer from substantial model error due to the absence of a proper consistency function, while self-consistency exhibits high estimation error due to a slow error convergence rate. To overcome these limitations, we propose Reasoning-Pruning Perplexity Consistency (RPC). This approach combines Perplexity Consistency, which seamlessly integrates LLM perplexity with self-consistency, and Reasoning Pruning, which eliminates low-probability reasoning paths to effectively prevent the degeneration of estimation error reduction. Theoretical analysis demonstrates that RPC not only accelerates the convergence rate of estimation error to an exponential level but also holds strong potential for further reducing model error. Extensive empirical evaluations on seven benchmark datasets confirm that RPC can significantly improve reasoning performance, sample efficiency, and confidence reliability.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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