Event sequences (ESs) arise in many practical domains including finance, retail, social networks, and healthcare. In the context of machine learning, event sequences can be seen as a special type of tabular data with annotated timestamps. Despite the importance of ESs modeling and analysis, little effort was made in adapting large language models (LLMs) to the ESs domain. In this paper, we highlight the common difficulties of ESs processing and propose a novel solution capable of solving multiple downstream tasks with little or no finetuning. In particular, we solve the problem of working with long sequences and improve time and numeric features processing. The resulting method, called ESQA, effectively utilizes the power of LLMs and, according to extensive experiments, achieves state-of-the-art results in the ESs domain.

翻译：事件序列（ESs）广泛出现在金融、零售、社交网络和医疗保健等多个实际领域。在机器学习背景下，事件序列可视为一种带有时间戳标注的特殊表格数据。尽管事件序列的建模与分析具有重要意义，但将大语言模型（LLMs）适配至事件序列领域的研究仍较为有限。本文系统阐述了事件序列处理的常见难点，并提出一种能够以极少或无需微调即可解决多种下游任务的新方法。该方法特别解决了长序列处理问题，并优化了时间与数值特征的处理流程。所提出的方法命名为ESQA，其有效利用了大语言模型的强大能力，并通过大量实验验证，在事件序列领域达到了最先进的性能水平。