Abstract: Event causality identification(ECI) is an important NLP task that aims to identify causal relationships between two events.However,due to the scarcity of causal data in public datasets,downstream ECI models have encountered bottlenecks.To alleviate the data scarcity problem,this paper proposes a large language model-based event causality data augmentation method(LLM-ECIAug).This method constructs a data generation strategy from two levels:causal event pairs and causal patterns.It utilizes large language models to generate diverse candidate augmentation data and combines an event causality filter fine-tuned on the original ECI datasets.In view of the differences between the distribution of candidate augmented data and the original data,a filtering mechanism based on KL divergence is introduced to rank and filter the generated data,retaining high-quality data that are most consistent with the original data distribution.Finally,the filtered augmented data is combined with the original data to train the downstream ECI model.Experimental results show that this method achieves better F1 scores than other data augmentation baseline methods on the Causal-TimeBank and EventStoryLine datasets,confirming its effectiveness and superiority.

Key words: Event causality identification, Data augmentation, Large language models, KL divergence, Text generation