Efficient Retrieval of Temporal Event Sequences from Textual Descriptions

• URL: http://arxiv.org/abs/2410.14043v1
• Date: Thu, 17 Oct 2024 21:35:55 GMT
• Title: Efficient Retrieval of Temporal Event Sequences from Textual Descriptions
• Authors: Zefang Liu, Yinzhu Quan,
• Abstract summary: TPP-LLM-Embedding is a unified model for embedding and retrieving event sequences based on natural language descriptions. Our model encodes both event types and times, generating a sequence-level representation through pooling. TPP-LLM-Embedding enables efficient retrieval and demonstrates superior performance compared to baseline models across diverse datasets.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Retrieving temporal event sequences from textual descriptions is essential for applications such as analyzing e-commerce behavior, monitoring social media activities, and tracking criminal incidents. In this paper, we introduce TPP-LLM-Embedding, a unified model for efficiently embedding and retrieving event sequences based on natural language descriptions. Built on the TPP-LLM framework, which integrates large language models with temporal point processes, our model encodes both event types and times, generating a sequence-level representation through pooling. Textual descriptions are embedded using the same architecture, ensuring a shared embedding space for both sequences and descriptions. We optimize a contrastive loss based on similarity between these embeddings, bringing matching pairs closer and separating non-matching ones. TPP-LLM-Embedding enables efficient retrieval and demonstrates superior performance compared to baseline models across diverse datasets.

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