TISE : A Tripartite In-context Selection Method for Event Argument Extraction (2024)

Abstract: In-context learning enhances the reasoning capabilities of LLMs by providing several examples. A direct yet effective approach to obtain in-context example is to select the top-k examples based on their semantic similarity to the test input. However, when applied to event argument extraction (EAE), this approach exhibits two shortcomings: 1) It may select almost identical examples, thus failing to provide additional event information, and 2) It overlooks event attributes, leading to the selected examples being unrelated to the test event type. In this paper, we introduce three necessary requirements when selecting an in-context example for EAE task: semantic similarity, example diversity and event correlation. And we further propose TISE, which scores examples from these three perspectives and integrates them using Determinantal Point Processes to directly select a set of examples as context. Experimental results on the ACE05 dataset demonstrate the effectiveness of TISE and the necessity of three requirements. Furthermore, we surprisingly observe that TISE can achieve superior performance with fewer examples and can even exceed some supervised methods.

## introduction event argument extraction (eae) aims to identify and classify event arguments within textual data based on the given event type. label for this task is a structured table in which each argument requires annotation. resulting in a scarcity of training data, which limits the generalizability of traditional methods to unknown samples @xcite . recently, large language models (llms) exemplified by gpt-3 @xcite have shown remarkable capabilities in low-resource scenarios. as shown in figure 1 , given the test ## method the overall structure of tise is shown in figure 2 , it measures semantic similarity and example diversity using the text scorer (scorer t ), and assesses event correlation using the event scorer (scorer e ). three scores are integrated into the kernel matrix k, and dpp is used to select an in-context example based on this. lastly, it employs code imitation prompt to conduct llmbased eae. ## discussion ## conclusion in this paper, we present three necessary requirements for the in-context example selection when using llm to conduct eae task. we propose tise, which scores the examples from three perspectives and leverages dpp to fuse these scores so that the optimal in-context example is directly selected. experiments on ace05 show tise can select a more efficient in-context example than baselines, is robust to the number of examples, and even can achieve better performance with fewer examples. tise can also be adapted to different prompts and llms, and outperforms some fully fine-tuned supervised methods. moreover, we explore the principle of the effectiveness of three requirements.