Layer-of-Thoughts Prompting (LoT)：利用基于 LLM 的检索与约束层次结构
Layer-of-Thoughts Prompting (LoT): Leveraging LLM-Based Retrieval with Constraint Hierarchies
AI总结：
本文提出 LoT 方法，利用约束层次结构过滤和优化候选响应，通过关注多轮交互中提示的层次关系，结合 LLM 显著提升了信息检索任务的准确性、可解释性和自动化。
摘要：
本文提出了一种名为 Layer-of-Thoughts (LoT) 的新方法，该方法利用约束层次结构来过滤和优化针对给定查询的候选响应。通过整合这些约束，我们的方法实现了结构化的检索过程，从而增强了可解释性和自动化。现有方法探索了各种提示技术，但通常呈现过于泛化的框架，而未深入探讨多轮交互中提示的细微差别。我们的工作通过关注提示之间的层次关系来填补这一空白。我们证明了思维层次结构的有效性在开发高效且可解释的检索算法中起着关键作用。利用大型语言模型 (LLMs)，LoT 显著提高了信息检索任务的准确性和可理解性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
