DyFIRER: A Dynamic Feedback Iterative Multi-Agent Framework for Open Relation Extraction Based on Large Language Models

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With the rapid evolution of Large Language Models (LLMs) in natural language understanding and generation, Relation Extraction (RE) has achieved substantial milestones in low-resource and open-domain scenarios. However, prevailing LLM-based RE methodologies predominantly rely on one-shot prompting or static workflows, which lack autonomous evaluation and iterative optimization mechanisms. Consequently, these approaches are prone to issues such as missing relations, type confusion, and factual hallucinations when navigating complex relational contexts. To address these limitations, this paper proposes DyFIRER (Dynamic Feedback Iterative Relation Extraction Framework), a multi-agent framework characterized by dynamic feedback. By constructing three functionally complementary agents—Extraction, Verification, and Optimization—the framework models the RE task as a closed-loop iterative process consisting of “extraction-verification-feedback-optimization,” thereby enabling dynamic adjustment and continuous refinement of extraction strategies. Experimental results on the DuIE 2.0 open relation extraction extension subset demonstrate that DyFIRER achieves an F1-score of 80.5%, modestly but statistically significantly outperforms GPT-4 (p = 0.014), a result that holds on both the augmented and non-augmented test sets and mainstream static methods (yielding a 10.3% improvement over Qwen-7B). Ablation studies further substantiate the critical role of the dynamic feedback iterative mechanism and the strategy retrieval module in mitigating complex relation omissions and factual hallucinations. The framework requires no additional annotated data or fine-tuning, suggesting potential applicability to low-resource settings, though this was not directly evaluated in the current study.