分层推理框架提升大模型解释力，让复杂决策更可信
Layered Chain-of-Thought Prompting for Multi-Agent LLM Systems: A Comprehensive Approach to Explainable Large Language Models
- 将推理过程拆分为多层，每层可外部验证或接收反馈
- 在医疗分诊等场景中，正确率和透明度显著优于传统方法
- 适合高风险领域，如医疗、金融，需可解释决策的场景
大型语言模型（LLM）采用思维链（CoT）提示来生成逐步推理，提升复杂任务表现。然而，原始CoT常无法充分验证中间推断，可能导致误导性解释。本文提出分层思维链（Layered-CoT）框架，系统性地将推理过程划分为多层，每层均可接受外部检查或用户反馈。我们基于医学分诊、金融风险评估和敏捷工程三个场景展开分析，证明该方法在透明度、正确性和用户参与度上均优于传统CoT。结合近期arXiv关于交互式可解释性、多智能体架构与协作机制的研究，说明该框架为高风险领域提供更可靠、更扎实的解释路径。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。