SHR: Enhancing Event Argument Extraction Ability of Large language Models with Simple-Hard Refining

Event Argument Extraction (EAE) aims to identify and extract key information such as entities, times, and locations related to specific events from text and serves as a fundamental task for many NLP applications. Recent researches have utilized large language models (LLMs) for EAE, effectively addressing the resource-intensive nature of annotating training datasets for this task. However, when performing EAE on longer texts (document-level EAE), the presence of descriptions unrelated to the events within document-level EAE can lead LLMs to identify incorrect arguments. To address this issue, we propose Simple-Hard Refining: a novel prompt framework that segments EAE into straightforward and complex extraction tasks. Based on the complexity of inference, we divide EAE task into simple-argument extraction and hard-argument extraction. By utilizing a chain of prompt to perform simple and hard argument extraction sequentially, noise introduced by irrelevant description for simple-argument extraction can be effectively alleviated. Furthermore, we explore the potential of LLMs to furnish dependable explanations for their extraction outcomes. We design an explanation-based prompting method that involves a three-step explanation process: relevant sentence extraction, argument role semantic analysis, and argument role entity localization. This method further enhances the extraction accuracy at each stage of the framework. Our experiments demonstrate that our method achieves state-of-the-art performance, surpassing various baselines that utilize LLMs for the EAE task. Ablation studies further verify the effectiveness of each stage of our framework and show the ability of our proposed approach to effectively mitigate noise. Our work contributes to the structured extraction of event argument information using LLMs.