An LLM-Based QA System for Chinese Painting and Calligraphy with Knowledge Graphs and External Documents | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article An LLM-Based QA System for Chinese Painting and Calligraphy with Knowledge Graphs and External Documents Jing Wan, Xinrong Li, Hao Zhang, Ao Zou, Rumei Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6824315/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 09 Dec, 2025 Read the published version in npj Heritage Science → Version 1 posted 9 You are reading this latest preprint version Abstract Chinese Painting and Calligraphy (ChP&C), key elements of Chinese cultural heritage, hold rich historical and artistic value. Although Large Language Models (LLMs) excel in open-domain question answering (QA), they often suffer from hallucinations in domain-specific contexts. To address this, we propose a ChP&C QA method integrating LLMs with a retrieval-augmented approach using a Knowledge Graph (KG) and external documents. The method decomposes complex questions into sub-questions and entities, retrieves relevant knowledge from KG and documents, and integrates answers via a dedicated module. We constructed a QA dataset focused on ChP&C to validate the proposed method. Additionally, a QA system was developed to systematically evaluate its performance in real-world applications. Experimental results demonstrate improved semantic understanding and answer accuracy by effectively combining structured and unstructured information. This system offers a reliable tool for accessing ChP&C knowledge and serves as a reference for intelligent QA in other cultural heritage domains. Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 09 Dec, 2025 Read the published version in npj Heritage Science → Version 1 posted Editorial decision: Revision requested 31 Jul, 2025 Reviews received at journal 20 Jul, 2025 Reviews received at journal 15 Jul, 2025 Reviewers agreed at journal 22 Jun, 2025 Reviewers agreed at journal 16 Jun, 2025 Reviewers invited by journal 16 Jun, 2025 Editor assigned by journal 12 Jun, 2025 Submission checks completed at journal 12 Jun, 2025 First submitted to journal 12 Jun, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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