Comparative Analysis of AI Agent Architectures for Entity Relationship Classification

• URL: http://arxiv.org/abs/2506.02426v2
• Date: Wed, 04 Jun 2025 14:21:02 GMT
• Title: Comparative Analysis of AI Agent Architectures for Entity Relationship Classification
• Authors: Maryam Berijanian, Kuldeep Singh, Amin Sehati,
• Abstract summary: In this study, we conduct a comparative analysis of three distinct AI agent architectures to perform relation classification.<n>The agentic architectures explored include (1) reflective self-evaluation, (2) hierarchical task decomposition, and (3) a novel multi-agent dynamic example generation mechanism.<n>Our experiments demonstrate that multi-agent coordination consistently outperforms standard few-shot prompting.
• Score: 1.6887793771613606
• License: http://creativecommons.org/licenses/by/4.0/
• 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 https://github.com/maryambrj/ALIEN.git.

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