HyST: LLM-Powered Hybrid Retrieval over Semi-Structured Tabular Data

• URL: http://arxiv.org/abs/2508.18048v1
• Date: Mon, 25 Aug 2025 14:06:27 GMT
• Title: HyST: LLM-Powered Hybrid Retrieval over Semi-Structured Tabular Data
• Authors: Jiyoon Myung, Jihyeon Park, Joohyung Han,
• Abstract summary: HyST (Hybrid retrieval over Semi-structured Tabular data) is a hybrid retrieval framework that combines structured filtering with semantic embedding search.<n>We show that HyST consistently outperforms tradtional baselines on a semi-structured benchmark.
• Score: 0.4779196219827507
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: User queries in real-world recommendation systems often combine structured constraints (e.g., category, attributes) with unstructured preferences (e.g., product descriptions or reviews). We introduce HyST (Hybrid retrieval over Semi-structured Tabular data), a hybrid retrieval framework that combines LLM-powered structured filtering with semantic embedding search to support complex information needs over semi-structured tabular data. HyST extracts attribute-level constraints from natural language using large language models (LLMs) and applies them as metadata filters, while processing the remaining unstructured query components via embedding-based retrieval. Experiments on a semi-structured benchmark show that HyST consistently outperforms tradtional baselines, highlighting the importance of structured filtering in improving retrieval precision, offering a scalable and accurate solution for real-world user queries.

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