Abstract
The integration of Large Language Models (LLMs) and knowledge graphs (KGs) has achieved remarkable success in various natural language processing tasks. However, existing methodologies that integrate LLMs and KGs often navigate the task-solving process solely based on the LLM's analysis of the question, overlooking the rich cognitive potential inherent in the vast knowledge encapsulated in KGs. To address this, we introduce Observation-Driven Agent (ODA), a novel AI agent framework tailored for tasks involving KGs. ODA incorporates KG reasoning abilities via global observation that enhances reasoning capabilities through a cyclical paradigm of observation, action, and reflection. Confronting the exponential explosion of knowledge during observation, we innovatively design a recursive observation mechanism. Subsequently, we integrate the observed knowledge into the action and reflection modules. Through extensive experiments, ODA demonstrates state-of-the-art performance on several datasets, notably achieving accuracy improvements of 12.87% and 8.9%.
Abstract (translated)
大规模语言模型（LLMs）与知识图（KGs）的集成在各种自然语言处理任务中取得了显著的成功。然而，现有的将LLMs与KGs集成的方法往往仅基于LLM对问题的分析来解决问题，而忽略了KGs中蕴含的丰富认知潜力。为解决这个问题，我们引入了观察驱动的智能体（ODA），一种专门针对涉及KGs的任务的AI框架。ODA通过通过全局观察来增强推理能力，采用观察、动作和反思的循环模式来解决观察膨胀的问题。在观察爆炸的过程中，我们创新地设计了一个递归的观察机制。然后，我们将观察到的知识集成到动作和反思模块中。通过大量实验，ODA在多个数据集上表现出与最先进方法相当的表现，尤其是在准确性方面，提高了12.87%和8.9%。
URL
https://arxiv.org/abs/2404.07677
