Event sequence models have been found to be highly effective in the analysis and prediction of events. Building such models requires availability of abundant high-quality event sequence data. In certain applications, however, clean structured event sequences are not available, and automated sequence extraction results in data that is too noisy and incomplete. In this work, we explore the use of Large Language Models (LLMs) to generate event sequences that can effectively be used for probabilistic event model construction. This can be viewed as a mechanism of distilling event sequence knowledge from LLMs. Our approach relies on a Knowledge Graph (KG) of event concepts with partial causal relations to guide the generative language model for causal event sequence generation. We show that our approach can generate high-quality event sequences, filling a knowledge gap in the input KG. Furthermore, we explore how the generated sequences can be leveraged to discover useful and more complex structured knowledge from pattern mining and probabilistic event models. We release our sequence generation code and evaluation framework, as well as corpus of event sequence data.

翻译：事件序列模型已被证实在事件分析和预测方面极为有效。构建此类模型需要充足且高质量的事件序列数据。然而，在某些应用场景中，由于缺乏干净的结构化事件序列，自动序列提取导致数据过于嘈杂且不完整。本研究探索利用大型语言模型（LLMs）生成可有效用于概率事件模型构建的事件序列。这可被视为从LLMs中提取事件序列知识的机制。我们的方法依赖于包含部分因果关系的知识图谱（KG）来引导生成式语言模型生成因果事件序列。实验表明，该方法能生成高质量的事件序列，填补输入KG中的知识空白。此外，我们进一步探索如何利用生成的序列从模式挖掘和概率事件模型中发掘更有用的复杂结构化知识。我们已公开发布序列生成代码、评估框架以及事件序列数据语料库。