Retrieval-Augmented Generation for Natural Language Art Provenance Searches in the Getty Provenance Index
On the map: Retrieval-augmented generation → Multimodal RAG
Abstract (arXiv)
This research presents a Retrieval-Augmented Generation (RAG) framework for art provenance studies, focusing on the Getty Provenance Index. Provenance research establishes the ownership history of artworks, which is essential for verifying authenticity, supporting restitution and legal claims, and understanding the cultural and historical context of art objects. The process is complicated by fragmented, multilingual archival data that hinders efficient retrieval. Current search portals require precise metadata, limiting exploratory searches. Our method enables natural-language and multilingual searches through semantic retrieval and contextual summarization, reducing dependence on metadata structures. We assess RAG's capability to retrieve and summarize auction records using a 10,000-record sample from the Getty Provenance Index - German Sales. The results show this approach provides a scalable solution for navigating art market archives, offering a practical tool for historians and cultural heritage professionals conducting historically sensitive research.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in Multimodal RAG, one of 15 directions in Retrieval-augmented generation. Builds on 19 of its 61 references that are in the map. First posted on arXiv 26 Aug 2025.
Builds on
19 of 61 references.
References · 19
- 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
- 2013Distributed Representations of Words and Phrases and their Compositionalitycited by 3,474
- 2017Billion-scale similarity search with GPUscited by 2,497
- 2023Retrieval-Augmented Generation for Large Language Models: A Surveycited by 2,212
- 2023Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflectioncited by 1,839
- 2023Active Retrieval Augmented Generationcited by 516
- 2024Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexitycited by 451
- 2023Ragas: Automated Evaluation of Retrieval Augmented Generationcited by 418
- 2024Multilingual E5 Text Embeddings: A Technical Reportcited by 372
- 2023Improving Text Embeddings with Large Language Modelscited by 303
- 2024ProSA: Assessing and Understanding the Prompt Sensitivity of LLMscited by 149
- 2024Evaluation of Retrieval-Augmented Generation: A Surveycited by 146
7 more
- 2024Evaluating Retrieval Quality in Retrieval-Augmented Generationcited by 90
- 2024Safeguarding Large Language Models: A Surveycited by 78
- 2024Don't Forget to Connect! Improving RAG with Graph-based Rerankingcited by 49
- 2024Beyond Benchmarks: Evaluating Embedding Model Similarity for Retrieval Augmented Generation Systemscited by 12
- 2024Enhancing Embedding Performance through Large Language Model-based Text Enrichment and Rewritingcited by 6
- 2024AI in Archival Science -- A Systematic Reviewcited by 2
- 2025MetaGen Blended RAG: Unlocking Zero-Shot Precision for Specialized Domain Question-Answeringcited by 2
Built on by
Citing papers per year: 2026: 1.
1 paper.
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- 2025Provenance Analysis of Archaeological Artifacts via Multimodal RAG Systems
- 2026Lot Machine: Multimodal Lot Extraction from Auction Catalogs
- 2026Improving Access to Historical Archives with Real-time RAG-based Systems ↗
- 2025A Preliminary Study of RAG for Taiwanese Historical Archives
- 2026M-RAG: Semantic Key-Value Indexing for Retrieval-Augmented Generation
- 2025Automating Iconclass: LLMs and RAG for Large-Scale Classification of Religious Woodcuts
- 2026MythraGen: Two-Stage Retrieval Augmented Art Generation Framework
- 2025ArtRAG: Retrieval-Augmented Generation with Structured Context for Visual Art Understanding