# ArchiveGPT: A human-centered evaluation of using a vision language model for image cataloguing

arXiv: https://arxiv.org/abs/2507.07551 · HTML: https://mlsanity.com/paper/2507.07551/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in VLM benchmarks and visual reasoning, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 8 of its 40 references that are in the map.
- First posted on arXiv 10 Jul 2025.

## Placed in

- Multimodal LLMs / vision-language models / [VLM benchmarks and visual reasoning](https://mlsanity.com/mllm/vlm-benchmarks-and-visual-reasoning/)

## Builds on (most cited first, 8 of 8)

- [A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications](https://mlsanity.com/paper/2402.07927/) (2024)
- [Enhancing the Reasoning Ability of Multimodal Large Language Models via Mixed Preference Optimization](https://mlsanity.com/paper/2411.10442/) (2024)
- [Human heuristics for AI-generated language are flawed](https://mlsanity.com/paper/2206.07271/) (2022)
- [The Participatory Turn in AI Design: Theoretical Foundations and the Current State of Practice](https://mlsanity.com/paper/2310.00907/) (2023)
- [Question-Driven Design Process for Explainable AI User Experiences](https://mlsanity.com/paper/2104.03483/) (2021)
- [Archives and AI: An Overview of Current Debates and Future Perspectives](https://mlsanity.com/paper/2105.01117/) (2021)
- [A Knowledge Base for Arts and Inclusion -- The Dataverse data archival platform as a knowledge base management system enabling multimodal accessibility](https://mlsanity.com/paper/2504.05976/) (2025)
- [Automated Archival Descriptions with Federated Intelligence of LLMs](https://mlsanity.com/paper/2504.05711/) (2025)

## Built on by (most cited first, 1 of 1)

- [OCR-Based Field Extraction for Archaeological Pottery Metadata: The CENTURIA Dataset](https://mlsanity.com/paper/2608.30616/) (2026)

## Abstract (arXiv)

The accelerating growth of photographic collections has outpaced manual cataloguing, motivating the use of vision language models (VLMs) to automate metadata generation. This study examines whether Al-generated catalogue descriptions can approximate human-written quality and how generative Al might integrate into cataloguing workflows in archival and museum collections. A VLM (InternVL2) generated catalogue descriptions for photographic prints on labelled cardboard mounts with archaeological content, evaluated by archive and archaeology experts and non-experts in a human-centered, experimental framework. Participants classified descriptions as AI-generated or expert-written, rated quality, and reported willingness to use and trust in AI tools. Classification performance was above chance level, with both groups underestimating their ability to detect Al-generated descriptions. OCR errors and hallucinations limited perceived quality, yet descriptions rated higher in accuracy and usefulness were harder to classify, suggesting that human review is necessary to ensure the accuracy and quality of catalogue descriptions generated by the out-of-the-box model, particularly in specialized domains like archaeological cataloguing. Experts showed lower willingness to adopt AI tools, emphasizing concerns on preservation responsibility over technical performance. These findings advocate for a collaborative approach where AI supports draft generation but remains subordinate to human verification, ensuring alignment with curatorial values (e.g., provenance, transparency). The successful integration of this approach depends not only on technical advancements, such as domain-specific fine-tuning, but even more on establishing trust among professionals, which could both be fostered through a transparent and explainable AI pipeline.