Can layered prompts improve LLM agent reasoning?
Layered Chain-of-Thought Prompting for Multi-Agent LLM Systems: A Comprehensive Approach to Explainable Large Language Models
This paper proposes Layered Chain-of-Thought (Layered-CoT) prompting, a method for making Large Language Models (LLMs) more reliable and explainable. Instead of generating a single chain of reasoning, Layered-CoT breaks the process into smaller steps (layers), each verified by external data or human feedback. This layered approach aims to catch errors early and improve the trustworthiness of LLM outputs, especially in complex domains like medicine or finance.
For multi-agent systems, Layered-CoT allows specialized agents to handle different tasks within each layer, such as fact-checking, knowledge retrieval, or user interaction. This agent specialization enhances the verification and correction process, leading to more robust and grounded explanations. The paper argues that Layered-CoT, particularly when combined with multi-agent systems, offers a more structured and trustworthy approach to reasoning compared to current methods.