Papers by Omar Zoloev
LATTE: Learning Aligned Transactions and Textual Embeddings for Bank Clients (2025.emnlp-industry)
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Egor Fadeev, Dzhambulat Mollaev, Aleksei Shestov, Dima Korolev, Omar Zoloev, Ivan A Kireev, Andrey Savchenko, Maksim Makarenko
| Challenge: | Large language models (LLMs) are computationally expensive and impractical for real-world pipelines. |
| Approach: | They propose a contrastive learning framework that aligns raw event embeddings with description-based semantic embedds from frozen LLMs. |
| Outcome: | The proposed framework outperforms state-of-the-art techniques for learning event sequence representations on real-world financial datasets while remaining deployable in latency-sensitive environments. |