Boundary-Aware LLM Augmentation for Low-Resource Event Argument Extraction

AbstractEvent argument extraction (EAE) is a crucial task in information extraction. However, its performance heavily depends on expensive annotated data, making data scarcity a persistent challenge. Data augmentation serves as an effective approach to improving model performance in low-resource settings, yet research on applying LLMs for EAE augmentation remains preliminary. In this study, we pay attention to the boundary sensitivity of EAE and investigate four LLM-based augmentation strategies: argument replacement, adjunction rewriting, their combination, and annotation generation. We conduct comprehensive experiments across four benchmark datasets, employing GPT-4o-Mini and DeepSeek-R1-7B as data generators. Our results show that boundary-aware augmentation consistently leads to greater performance improvements over boundary-agnostic methods. In addition to performance gains, we provide a detailed analysis of augmentation quality from multiple perspectives, including uncertainty reduction, error types, data quality, and data scale. This work offers both empirical evidence and practical guidance for leveraging LLMs to enhance event argument extraction under low-resource conditions.