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arXiv · 2508.05688

LLM4ES: Learning User Embeddings from Event Sequences via Large Language Models

Aleksei Shestov·Omar Zoloev·Maksim Makarenko·Mikhail Orlov·Egor Fadeev·Ivan Kireev·Andrey Savchenko
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Abstract

This paper presents LLM4ES, a novel framework that exploits large pre-trained language models (LLMs) to derive user embeddings from event sequences. Event sequences are transformed into a textual representation, which is subsequently used to fine-tune an LLM through next-token prediction to generate high-quality embeddings. We introduce a text enrichment technique that enhances LLM adaptation to event sequence data, improving representation quality for low-variability domains. Experimental results demonstrate that LLM4ES achieves state-of-the-art performance in user classification tasks in financial and other domains, outperforming existing embedding methods. The resulting user embeddings can be incorporated into a wide range of applications, from user segmentation in finance to patient outcome prediction in healthcare.

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Aleksei Shestov, Omar Zoloev, Maksim Makarenko, Mikhail Orlov, Egor Fadeev, Ivan Kireev, Andrey Savchenko. 2025-08-06. LLM4ES: Learning User Embeddings from Event Sequences via Large Language Models. https://arxiv.org/abs/2508.05688

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