Exploring Instruction Feature Adaptation for Event Argument Extraction in Large Language Models

Event argument extraction (EAE) is an important and critical task in natural language processing, whose goal is to transform unstructured text into structured information. However, previous methods are costly for annotation, which limits their application. With the emergence of large language models (LLMs), many approaches use prompt learning or instruction learning to solve the EAE task. However, due to the complexity of the EAE, the defined instructions are often abstract and difficult for the model to accurately understand the task objective, while the cost of gradient propagation based methods is too high. Therefore, we propose an Instruction Feature Adaptation event argument extraction (IFA) that uses very few shots to tune the task instruction features, while not requiring a highcost gradient optimisation method to enhance the model’s performance on the event argument extraction task. Experiments demonstrate that the method is able to optimise the instruction features, enabling the model to perform the event argument task more consistently based on the instructions, ultimately improving performance for low-resource. Code is available at https://github.com/yangzonghao1024/IFA.