Abstract
In the field of Question Answering (QA), unifying large language models (LLMs) with external databases has shown great success. However, these methods often fall short in providing the advanced reasoning needed for complex QA tasks. To address these issues, we improve over a novel approach called Knowledge Graph Prompting (KGP), which combines knowledge graphs with a LLM-based agent to improve reasoning and search accuracy. Nevertheless, the original KGP framework necessitates costly fine-tuning with large datasets yet still suffers from LLM hallucination. Therefore, we propose a reasoning-infused LLM agent to enhance this framework. This agent mimics human curiosity to ask follow-up questions to more efficiently navigate the search. This simple modification significantly boosts the LLM performance in QA tasks without the high costs and latency associated with the initial KGP framework. Our ultimate goal is to further develop this approach, leading to more accurate, faster, and cost-effective solutions in the QA domain.
Abstract (translated)
在问题回答（QA）领域，将大型语言模型（LLMs）与外部数据库统一的做法取得了巨大的成功。然而，这些方法在提供复杂QA任务所需的高级推理方面常常不足。为解决这些问题，我们改进了一种名为知识图谱提示（KGP）的新方法，该方法将知识图谱与基于LLM的代理相结合以提高推理和搜索精度。然而，原始KGP框架需要对大量数据进行昂贵的微调，但仍存在LLM幻觉的问题。因此，我们提出了一个基于推理的LLM代理以增强这一框架。这个代理模仿人类的好奇心，以更有效地引导搜索。这样的简单修改在不需要初始KGP框架的高昂成本和延迟的情况下显著提高了LLM在QA任务中的性能。我们的最终目标是进一步发展这种方法，为QA领域提供更准确、更快、更经济有效的解决方案。
URL
https://arxiv.org/abs/2404.09077
