迈向高效的思维链蒸馏：用自引导理由选择器以更少理由实现更好性能
Towards Efficient CoT Distillation: Self-Guided Rationale Selector for Better Performance with Fewer Rationales
AI总结：
针对CoT蒸馏忽视理由质量的问题，本文提出MoRSD自引导选择高质量理由，并以RD指标控制难度，用更少数据在七个数据集上平均提升4.6%。
摘要：
思维链（Chain-of-thought, CoT）蒸馏旨在通过将较大教师模型的多步推理能力迁移给小型语言模型（SLM），从而增强其推理能力。然而，现有工作低估了理由质量的重要性，主要关注数据数量，这可能会将噪声或错误信息迁移给学生模型。为解决上述问题，我们提出了面向模型的理由选择蒸馏（Model-Oriented Rationale Selection Distillation, MoRSD），它能够识别并选择高质量理由用于蒸馏，以进一步提升性能。我们进一步提出理由难度（Rationale Difficulty, RD）指标，用于衡量学生模型在给定理由下生成正确答案的能力。与基线相比，我们通过控制理由的准确性、多样性和难度，使用更少的理由，在三个任务的七个数据集上取得了4.6%的平均提升。结果表明，与使用整个数据集相比，一小部分高质量理由能够增强学生模型的推理能力。我们的方法有望成为高效CoT蒸馏的一种可行解决方案。代码将发布于 https://github.com/Leon221220/MoRSD。
英文摘要：
Chain-of-thought (CoT) distillation aims to enhance small language models' (SLMs) reasoning by transferring multi-step reasoning capability from the larger teacher models. However, existing work underestimates rationale quality, focusing primarily on data quantity, which may transfer noisy or incorrect information to the student model. To address the above issues, we proposed \textbf{M}odel-\textbf{O}riented \textbf{R}ationale \textbf{S}election \textbf{D}istillation (MoRSD), which can discern and select high quality rationales for distillation to improve performance further. We further propose a Rationale Difficulty (RD) metric to measure the ability of the student model to generate the correct answer under a given rationale. Compared to the baseline, we achieved 4.6$\%$ average improvement on seven datasets over three tasks, using fewer rationales by controlling their accuracy, diversity, and difficulty. Our results reveal that a small portion of the high quality rationales can enhance the reasoning ability of student models than the entire dataset. Our method promises to be a possible solution for efficient CoT distillation. Our code will be released in https://github.com/Leon221220/MoRSD.
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。
