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Chain-of-Thought (CoT) is a technique that guides Large Language Models(LLMs) to decompose complex tasks into multi-step reasoning throughintermediate steps in natural language form. Briefly, CoT enables LLMs to thinkstep by step. However, although many Natural Language Understanding (NLU) tasksalso require thinking step by step, LLMs perform less well than small-scaleMasked Language Models (MLMs). To migrate CoT from LLMs to MLMs, we proposeChain-of-Thought Tuning (CoTT), a two-step reasoning framework based on prompttuning, to implement step-by-step thinking for MLMs on NLU tasks. From theperspective of CoT, CoTT's two-step framework enables MLMs to implement taskdecomposition; CoTT's prompt tuning allows intermediate steps to be used innatural language form. Thereby, the success of CoT can be extended to NLU tasksthrough MLMs. To verify the effectiveness of CoTT, we conduct experiments ontwo NLU tasks: hierarchical classification and relation extraction, and theresults show that CoTT outperforms baselines and achieves state-of-the-artperformance.

Chain-of-Thought Tuning: Masked Language Models can also Think Step By Step in Natural Language Understanding