TPP-LLM: Modeling Temporal Point Processes by Efficiently Fine-Tuning Large Language Models

Temporal point processes (TPPs) are widely used to model the timing and occurrence of events in domains such as social networks, transportation systems, and e-commerce. In this paper, we introduce TPP-LLM, a novel framework that integrates large language models (LLMs) with TPPs to capture both the semantic and temporal aspects of event sequences. Unlike traditional methods that rely on categorical event type representations, TPP-LLM directly utilizes the textual descriptions of event types, enabling the model to capture rich semantic information embedded in the text. While LLMs excel at understanding event semantics, they are less adept at capturing temporal patterns. To address this, TPP-LLM incorporates temporal embeddings and employs parameter-efficient fine-tuning (PEFT) methods to effectively learn temporal dynamics without extensive retraining. This approach improves both predictive accuracy and computational efficiency. Experimental results across diverse real-world datasets demonstrate that TPP-LLM outperforms state-of-the-art baselines in sequence modeling and event prediction, highlighting the benefits of combining LLMs with TPPs. Temporal point processes (TPPs) (Shchur et al., 2021) are powerful tools for modeling the occurrence of events over time, with widespread applications in domains such as social networks, urban dynamics, transportation, natural disasters, and e-commerce. The challenge of predicting both the type and timing of future events based on historical sequences has led to the development of increasingly sophisticated models. Traditional TPP models often rely on handcrafted features or specific assumptions about temporal dependencies, which limit their ability to capture complex event patterns in real-world datasets.