Language-TPP: Integrating Temporal Point Processes with Language Models for Event Analysis

• URL: http://arxiv.org/abs/2502.07139v1
• Date: Tue, 11 Feb 2025 00:09:45 GMT
• Title: Language-TPP: Integrating Temporal Point Processes with Language Models for Event Analysis
• Authors: Quyu Kong, Yixuan Zhang, Yang Liu, Panrong Tong, Enqi Liu, Feng Zhou,
• Abstract summary: Temporal Point Processes (TPPs) have been widely used for event sequence modeling, but they often struggle to incorporate rich textual event descriptions effectively.<n>We introduce Language-TPP, a unified framework that integrates TPPs with Large Language Models (LLMs) for enhanced event sequence modeling.
• Score: 23.27520345839548
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Temporal Point Processes (TPPs) have been widely used for event sequence modeling, but they often struggle to incorporate rich textual event descriptions effectively. Conversely, while Large Language Models (LLMs) have been shown remarkable capabilities in processing textual data, they lack mechanisms for handling temporal dynamics. To bridge this gap, we introduce Language-TPP, a unified framework that integrates TPPs with LLMs for enhanced event sequence modeling. Language-TPP introduces a novel temporal encoding mechanism that converts continuous time intervals into specialized byte-tokens, enabling seamless integration with standard LLM architectures. This approach allows Language-TPP to achieve state-of-the-art performance across multiple TPP tasks, including event time prediction, type prediction, and intensity estimation, on five datasets. Additionally, we demonstrate that incorporating temporal information significantly improves the quality of generated event descriptions.

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