Meta-CoT: Generalizable Chain-of-Thought Prompting in Mixed-task Scenarios with Large Language Models
Meta-CoT is a generalizable CoT prompting method in mixed-task scenarios where the type of input questions is unknown. It consists of three phases: (i) scenario identification: categorizes the scenario of the input question; (ii) demonstration selection: fetches the ICL demonstrations for the categorized scenario; (iii) answer derivation: performs the answer inference by feeding the LLM with the prompt comprising the fetched ICL demonstrations and the input question
