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

Aleksei Shestov, Omar Zoloev, Maksim Makarenko, Mikhail Orlov, Egor Fadeev, Ivan Kireev, Andrey V. Savchenko

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.

External IDs:dblp:journals/corr/abs-2508-05688