让大模型生成时主动纠错，省字又更准
A Stitch in Time Saves Nine: Proactive Self-Refinement for Language Models
- 模型边生成边判断是否需要修正，动态决定优化时机
- 在Qwen3-8B上减少41.6%的token消耗，准确率提升8.2%
- 适合追求高效精准生成的开发者与研究者
自精炼技术通过迭代优化显著提升了大语言模型（LLM）的输出质量。然而，现有方法多采用固定次数的被动式迭代，难以根据生成过程中的上下文变化动态调整优化时机与内容。受人类执行任务时持续反思行为启发，我们提出主动自精炼（PASR），使模型能在生成过程中主动决策何时、如何及是否进行修正。与重新生成整段输出的方法不同，PASR基于模型内部状态和演化上下文做出判断。我们在10个多样化任务上进行了广泛实验，结果表明，PASR显著提升了问题求解性能。尤其在Qwen3-8B上，相比标准生成，平均令牌消耗降低41.6%，准确率提升8.2%。论文代码与基线已开源。
原文摘要 · Abstract (English)
Recent advances in self-refinement have demonstrated significant potential for improving the outputs of large language models (LLMs) through iterative refinement. However, most existing self-refinement methods rely on a reactive process with a fixed number of iterations, making it difficult to determine the optimal timing and content of refinement based on the evolving generation context. Inspired by the way humans dynamically refine their thoughts during execution, we propose ProActive Self-Refinement (PASR), a novel method that enables LLMs to refine their outputs during the generation process. Unlike methods that regenerate entire responses, PASR proactively decides whether, when, and how to refine based on the model's internal state and evolving context. We conduct extensive experiments on a diverse set of 10 tasks to evaluate the effectiveness of PASR. Experimental results show that PASR significantly enhances problem-solving performance. In particular, on Qwen3-8B, PASR reduces average token consumption by 41.6% compared to standard generation, while also achieving an 8.2% improvement in accuracy. Our code and baselines used in the paper are available on GitHub.
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