Abstract
Entity relationship classification remains a challenging task in information extraction, especially in scenarios with limited labeled data and complex relational structures. In this study, we conduct a comparative analysis of three distinct AI agent architectures designed to perform relation classification using large language models (LLMs). The agentic architectures explored include (1) reflective self-evaluation, (2) hierarchical task decomposition, and (3) a novel multi-agent dynamic example generation mechanism, each leveraging different modes of reasoning and prompt adaptation. In particular, our dynamic example generation approach introduces real-time cooperative and adversarial prompting. We systematically compare their performance across multiple domains and model backends. Our experiments demonstrate that multi-agent coordination consistently outperforms standard few-shot prompting and approaches the performance of fine-tuned models. These findings offer practical guidance for the design of modular, generalizable LLM-based systems for structured relation extraction. The source codes and dataset are available at \href{this https URL}{this https URL}.
Abstract (translated)
实体关系分类在信息抽取中仍是一项具有挑战性的任务，特别是在标签数据有限且关系结构复杂的情况下。在这项研究中，我们对比分析了三种不同的AI代理架构，这些架构旨在使用大型语言模型（LLMs）执行关系分类。探讨的代理架构包括：(1) 反思性自我评估、(2) 分层任务分解以及 (3) 一种新颖的多代理动态示例生成机制，每种方法都利用了不同的推理和提示调整模式。特别是，我们的动态示例生成方法引入了实时协作和对抗性的提示技术。我们系统地比较了这些架构在多个领域和模型后端上的性能表现。实验结果表明，多代理协调一致优于标准的少量样本提示，并接近于微调模型的表现水平。这些发现为设计模块化、通用化的LLM基结构化关系抽取系统的实际应用提供了指导方针。源代码和数据集可在 [此链接](https://this%20URL) 获取。
URL
https://arxiv.org/abs/2506.02426
