Layered Chain-of-Thought Prompting for Multi-Agent LLM Systems: A Comprehensive Approach to Explainable Large Language Models
It addresses the need for more reliable and grounded explanations in high-stakes domains such as healthcare and finance, representing an incremental improvement over existing methods.
The paper tackles the problem of vanilla chain-of-thought prompting failing to verify intermediate inferences and producing misleading explanations by proposing Layered Chain-of-Thought Prompting, which segments reasoning into layers with external checks and user feedback, demonstrating improved transparency, correctness, and user engagement in scenarios like medical triage and financial risk assessment.
Large Language Models (LLMs) leverage chain-of-thought (CoT) prompting to provide step-by-step rationales, improving performance on complex tasks. Despite its benefits, vanilla CoT often fails to fully verify intermediate inferences and can produce misleading explanations. In this work, we propose Layered Chain-of-Thought (Layered-CoT) Prompting, a novel framework that systematically segments the reasoning process into multiple layers, each subjected to external checks and optional user feedback. We expand on the key concepts, present three scenarios -- medical triage, financial risk assessment, and agile engineering -- and demonstrate how Layered-CoT surpasses vanilla CoT in terms of transparency, correctness, and user engagement. By integrating references from recent arXiv papers on interactive explainability, multi-agent frameworks, and agent-based collaboration, we illustrate how Layered-CoT paves the way for more reliable and grounded explanations in high-stakes domains.