Chain-of-Thought (CoT) is a technique that guides Large Language Models (LLMs) to decompose complex tasks into multi-step reasoning through intermediate steps in natural language form. Briefly, CoT enables LLMs to think step by step. However, although many Natural Language Understanding (NLU) tasks also require thinking step by step, LLMs perform less well than small-scale Masked Language Models (MLMs). To migrate CoT from LLMs to MLMs, we propose Chain-of-Thought Tuning (CoTT), a two-step reasoning framework based on prompt tuning, to implement step-by-step thinking for MLMs on NLU tasks. From the perspective of CoT, CoTT's two-step framework enables MLMs to implement task decomposition; CoTT's prompt tuning allows intermediate steps to be used in natural language form. Thereby, the success of CoT can be extended to NLU tasks through MLMs. To verify the effectiveness of CoTT, we conduct experiments on two NLU tasks: hierarchical classification and relation extraction, and the results show that CoTT outperforms baselines and achieves state-of-the-art performance.

翻译：思维链（CoT）是一种通过自然语言形式的中间步骤，引导大型语言模型（LLMs）将复杂任务分解为多步推理的技术。简而言之，CoT使LLMs能够逐步思考。然而，尽管许多自然语言理解（NLU）任务同样需要逐步思考，但LLMs的表现不如小规模掩码语言模型（MLMs）。为将CoT从LLMs迁移至MLMs，我们提出思维链调优（CoTT），一种基于提示调优的两步推理框架，旨在为MLMs在NLU任务中实现逐步思考。从CoT视角看，CoTT的两步框架使MLMs能够实现任务分解；CoTT的提示调优允许中间步骤以自然语言形式使用。由此，CoT的成功可通过MLMs扩展到NLU任务。为验证CoTT的有效性，我们在层次分类和关系抽取两个NLU任务上进行实验，结果表明CoTT优于基线方法并达到最先进性能。