用无标签数据生成可信推理链，提升大模型少样本推理能力
Revisiting Chain-of-Thought Reasoning under Limited Supervision: Semi-supervised Chain-of-Thought Learning
- 从无标签问题生成多个伪推理链，按语义熵筛选高精度版本
- 在AQuA等数据集上伪答案准确率达91.36%至100%
- 适合想用少量标注数据提升模型推理能力的研究者
链式思维（CoT）推理已成为激发大语言模型潜在推理能力的有效方法。然而，现有方法多将推理链仅用于推理阶段提示，生成的推理过程很少作为半监督学习信号复用。本文提出「半监督链式思维学习」（Semi-CoT），通过无标签问题构建伪推理监督信号。该框架为每个无标签问题采样多个伪CoT，计算答案级语义熵，选择低熵推理链作为可靠伪示例。实验表明，在AQuA、SVAMP、GSM8K和MultiArith上，所选伪答案精度达91.36%至100%，证明无标签数据可提供可靠推理信号。尽管如此，模型在不同数据集表现不一：SVAMP与GSM8K略有提升，而AQuA出现负迁移，MultiArith已接近性能天花板。结果说明伪监督有效，但需更优示例筛选或学生模型训练策略。
原文摘要 · Abstract (English)
Chain-of-thought (CoT) reasoning has emerged as an effective approach for activating latent reasoning capabilities in large language models. However, most existing CoT methods use reasoning chains mainly as inference-time prompts, while the generated reasoning traces are rarely reused as semi-supervised learning signals. In this report, we define \textbf{Semi-supervised Chain-of-Thought Learning} and propose \textbf{Semi-CoT}, a simple framework that uses unlabeled questions to construct pseudo reasoning supervision. Semi-CoT samples multiple pseudo-CoTs for each unlabeled question, estimates answer-level semantic entropy, and selects low-entropy reasoning chains as reliable pseudo-CoT demonstrations. This extends the self-training view of CoT from inference-time refinement to semi-supervised pseudo-supervision. Pilot experiments on AQuA, SVAMP, GSM8K, and MultiArith show that the entropy gate selects high-precision pseudo-CoTs, with pseudo-answer precision ranging from $91.36\%$ to $100\%$. Semi-CoT also gives small gains on SVAMP and GSM8K, while AQuA shows negative transfer and MultiArith reaches a ceiling. These results suggest that unlabeled questions can provide reliable pseudo reasoning signals, but their effective use still requires stronger demonstration selection or student training.
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