Comparative Analysis of AI Agent Architectures for Entity Relationship Classification
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866911533110394880 |
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| author | Berijanian, Maryam Singh, Kuldeep Sehati, Amin |
| author_facet | Berijanian, Maryam Singh, Kuldeep Sehati, Amin |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2506_02426 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Comparative Analysis of AI Agent Architectures for Entity Relationship Classification Berijanian, Maryam Singh, Kuldeep Sehati, Amin Computation and Language Artificial Intelligence I.2.7; I.2.1 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. |
| title | Comparative Analysis of AI Agent Architectures for Entity Relationship Classification |
| topic | Computation and Language Artificial Intelligence I.2.7; I.2.1 |
| url | https://arxiv.org/abs/2506.02426 |