Related papers: Layer-of-Thoughts Prompting (LoT): Leveraging LLM-Based Retrieval with Constraint Hierarchies

URL: http://arxiv.org/abs/2410.12153v1
Date: Wed, 16 Oct 2024 01:20:44 GMT
Title: Layer-of-Thoughts Prompting (LoT): Leveraging LLM-Based Retrieval with Constraint Hierarchies
Authors: Wachara Fungwacharakorn, Nguyen Ha Thanh, May Myo Zin, Ken Satoh,
Abstract summary: Layer-of-Thoughts Prompting (LoT) uses constraint hierarchies to filter and refine candidate responses to a given query. LoT significantly improves the accuracy and comprehensibility of information retrieval tasks.
Score: 0.3946282433423277
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Abstract: 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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