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 pre-defined task-specific demonstrations to attain preferable performances, thereby engendering an inescapable gap between performance and generalization. To bridge this gap, we propose GeM-CoT, a Generalizable CoT prompting mechanism in Mixed-task scenarios where the type of input questions is unknown. GeM-CoT first categorizes the question type and subsequently samples or constructs demonstrations from the corresponding data pool in an automatic pattern. With this technical design, GeM-CoT simultaneously enjoys superior generalization capabilities and remarkable performances on 10 public reasoning tasks and 23 BBH tasks.

翻译：大语言模型通过利用思维链提示展现了卓越的推理能力，该方法生成中间推理链作为推导答案的依据。然而现有思维链方法要么简单使用"让我们一步步思考"等通用提示，要么严重依赖预定义任务特定示例以获得理想性能，从而在性能与泛化能力之间产生了不可避免的鸿沟。为弥合这一差距，我们提出了GeM-CoT——一种面向输入问题类型未知的混合任务场景的可推广思维链提示机制。GeM-CoT首先对问题类型进行分类，随后通过自动化模式从对应数据池中采样或构建示例。凭借这一技术设计，GeM-CoT在10个公开推理任务和23个BBH任务上同时展现出卓越的泛化能力和显著性能。