思维链微调：掩码语言模型也能在自然语言理解中逐步思考
Chain-of-Thought Tuning: Masked Language Models can also Think Step By Step in Natural Language Understanding
AI总结：
本文提出基于提示微调的两步推理框架CoTT，将思维链技术迁移至掩码语言模型，使其在层次分类和关系提取等自然语言理解任务中实现逐步思考，性能优于基线并达到SOTA。
摘要：
思维链（CoT）是一种引导大型语言模型（LLMs）将复杂任务分解为多步推理的技术，通过自然语言形式的中间步骤实现。简言之，CoT使LLMs能够逐步思考。然而，尽管许多自然语言理解（NLU）任务也需要逐步思考，但LLMs的表现不如小规模掩码语言模型（MLMs）。为了将CoT从LLMs迁移到MLMs，我们提出了思维链微调（CoTT），这是一种基于提示微调的两步推理框架，用于在NLU任务中实现MLMs的逐步思考。从CoT的角度来看，CoTT的两步框架使MLMs能够实现任务分解；CoTT的提示微调允许中间步骤以自然语言形式使用。从而，CoT的成功可以通过MLMs扩展到NLU任务。为了验证CoTT的有效性，我们在两个NLU任务上进行了实验：层次分类和关系提取，结果表明CoTT优于基线并达到了最先进的性能。
英文摘要：
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.
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。
