An LLM-based QA system for Chinese Painting and Calligraphy with Knowledge Graphs and external documents

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.