用分层约束优化大模型问答，提升检索准确性和可解释性。
Layer-of-Thoughts Prompting (LoT): Leveraging LLM-Based Retrieval with Constraint Hierarchies
- 通过分层约束筛选候选答案，构建结构化推理流程。
- 在多轮交互中显著提升检索准确率与结果可理解性。
- 适合需要透明决策过程的智能客服、法律咨询等场景。
本文提出一种名为分层思维提示（Layer-of-Thoughts Prompting, LoT）的新方法，利用约束层次结构过滤并优化对给定查询的候选回答。通过整合这些约束，该方法实现了结构化的检索过程，增强了可解释性与自动化能力。现有方法虽探索了多种提示技术，但通常缺乏对多轮交互中提示细节的深入分析。本工作填补这一空白，聚焦提示间的层级关系，实证表明思维层次的有效性在构建高效且可解释的检索算法中起关键作用。借助大语言模型（LLMs），LoT显著提升了信息检索任务的准确率与可理解性。
原文摘要 · Abstract (English)
This paper presents a novel approach termed Layer-of-Thoughts Prompting (LoT), which utilizes constraint hierarchies to filter and refine candidate responses to a given query. By integrating these constraints, our method enables a structured retrieval process that enhances explainability and automation. Existing methods have explored various prompting techniques but often present overly generalized frameworks without delving into the nuances of prompts in multi-turn interactions. Our work addresses this gap by focusing on the hierarchical relationships among prompts. We demonstrate that the efficacy of thought hierarchy plays a critical role in developing efficient and interpretable retrieval algorithms. Leveraging Large Language Models (LLMs), LoT significantly improves the accuracy and comprehensibility of information retrieval tasks.
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