https://arxiv.org/abs/2405.04674 Skip to main content Cornell University We gratefully acknowledge support from the Simons Foundation, member institutions, and all contributors. Donate arxiv logo > cs > arXiv:2405.04674 [ ] Help | Advanced Search [All fields ] Search arXiv logo Cornell University Logo [ ] GO quick links * Login * Help Pages * About Computer Science > Databases arXiv:2405.04674 (cs) [Submitted on 7 May 2024] Title:Towards Accurate and Efficient Document Analytics with Large Language Models Authors:Yiming Lin, Madelon Hulsebos, Ruiying Ma, Shreya Shankar, Sepanta Zeigham, Aditya G. Parameswaran, Eugene Wu View a PDF of the paper titled Towards Accurate and Efficient Document Analytics with Large Language Models, by Yiming Lin and 6 other authors View PDF HTML (experimental) Abstract:Unstructured data formats account for over 80% of the data currently stored, and extracting value from such formats remains a considerable challenge. In particular, current approaches for managing unstructured documents do not support ad-hoc analytical queries on document collections. Moreover, Large Language Models (LLMs) directly applied to the documents themselves, or on portions of documents through a process of Retrieval-Augmented Generation (RAG), fail to provide high accuracy query results, and in the LLM-only case, additionally incur high costs. Since many unstructured documents in a collection often follow similar templates that impart a common semantic structure, we introduce ZenDB, a document analytics system that leverages this semantic structure, coupled with LLMs, to answer ad-hoc SQL queries on document collections. ZenDB efficiently extracts semantic hierarchical structures from such templatized documents, and introduces a novel query engine that leverages these structures for accurate and cost-effective query execution. Users can impose a schema on their documents, and query it, all via SQL. Extensive experiments on three real-world document collections demonstrate ZenDB's benefits, achieving up to 30% cost savings compared to LLM-based baselines, while maintaining or improving accuracy, and surpassing RAG-based baselines by up to 61% in precision and 80% in recall, at a marginally higher cost. Subjects: Databases (cs.DB) Cite as: arXiv:2405.04674 [cs.DB] (or arXiv:2405.04674v1 [cs.DB] for this version) https://doi.org/10.48550/arXiv.2405.04674 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Yiming Lin [view email] [v1] Tue, 7 May 2024 21:14:38 UTC (5,034 KB) Full-text links: Access Paper: View a PDF of the paper titled Towards Accurate and Efficient Document Analytics with Large Language Models, by Yiming Lin and 6 other authors * View PDF * HTML (experimental) * TeX Source * Other Formats license icon view license Current browse context: cs.DB < prev | next > new | recent | 2405 Change to browse by: cs References & Citations * NASA ADS * Google Scholar * Semantic Scholar a export BibTeX citation Loading... BibTeX formatted citation x [loading... ] Data provided by: Bookmark BibSonomy logo Reddit logo (*) Bibliographic Tools Bibliographic and Citation Tools [ ] Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) 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