Large language models (LLMs) have unveiled remarkable reasoning capabilities
by exploiting chain-of-thought (CoT) prompting, which generates intermediate
reasoning chains to serve as the rationale for deriving the answer. However,
current CoT methods either simply employ general prompts such as Let's think
step by step, or heavily rely on handcrafted task-specific demonstrations to
attain preferable performances, thereby engendering an inescapable gap between
performance and generalization. To bridge this gap, we propose Meta-CoT, a
generalizable CoT prompting method in mixed-task scenarios where the type of
input questions is unknown. Meta-CoT firstly categorizes the scenario based on
the input question and subsequently constructs diverse demonstrations from the
corresponding data pool in an automatic pattern. Meta-CoT simultaneously enjoys
remarkable performances on ten public benchmark reasoning tasks and superior
generalization capabilities. Notably, Meta-CoT achieves the state-of-the-art
result on SVAMP (93.7%) without any additional program-aided methods. Our
further experiments on five out-of-distribution datasets verify the stability
and generality of Meta-CoT.

使用混合任务场景中的元推理链 (Meta-CoT) 提示方法，构建来自相应数据池的多样化演示，可同时在十个公共基准推理任务上表现出卓越的性能和优越的泛化能力。