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arXiv · 2505.24553

CREFT: Sequential Multi-Agent LLM for Character Relation Extraction

Ye Eun Chun·Taeyoon Hwang·Seung-won Hwang·Byung-Hak Kim
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Abstract

Understanding complex character relations is crucial for narrative analysis and efficient script evaluation, yet existing extraction methods often fail to handle long-form narratives with nuanced interactions. To address this challenge, we present CREFT, a novel sequential framework leveraging specialized Large Language Model (LLM) agents. First, CREFT builds a base character graph through knowledge distillation, then iteratively refines character composition, relation extraction, role identification, and group assignments. Experiments on a curated Korean drama dataset demonstrate that CREFT significantly outperforms single-agent LLM baselines in both accuracy and completeness. By systematically visualizing character networks, CREFT streamlines narrative comprehension and accelerates script review -- offering substantial benefits to the entertainment, publishing, and educational sectors.

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Ye Eun Chun, Taeyoon Hwang, Seung-won Hwang, Byung-Hak Kim. 2025-05-30. CREFT: Sequential Multi-Agent LLM for Character Relation Extraction. https://arxiv.org/abs/2505.24553

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