mlsanityData to 14 Oct 2026

Generative AI in Heritage Practice: Improving the Accessibility of Heritage Guidance

arXiv 2510.13811 ↗First posted 3 Sep 2025cs.HC cs.AI cs.CLcited by 0

Not on the map. Why?

Abstract (arXiv)

This paper discusses the potential for integrating Generative Artificial Intelligence (GenAI) into professional heritage practice with the aim of enhancing the accessibility of public-facing guidance documents. We developed HAZEL, a GenAI chatbot fine-tuned to assist with revising written guidance relating to heritage conservation and interpretation. Using quantitative assessments, we compare HAZEL's performance to that of ChatGPT (GPT-4) in a series of tasks related to the guidance writing process. The results of this comparison indicate a slightly better performance of HAZEL over ChatGPT, suggesting that the GenAI chatbot is more effective once the underlying large language model (LLM) has been fine-tuned. However, we also note significant limitations, particularly in areas requiring cultural sensitivity and more advanced technical expertise. These findings suggest that, while GenAI cannot replace human heritage professionals in technical authoring tasks, its potential to automate and expedite certain aspects of guidance writing could offer valuable benefits to heritage organisations, especially in resource-constrained contexts.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 14 of its 156 references that are in the map. First posted on arXiv 3 Sep 2025.

Builds on

14 of 156 references.

References · 14

  1. 2024How Johnny Can Persuade LLMs to Jailbreak Them: Rethinking Persuasion to Challenge AI Safety by Humanizing LLMscited by 573
  2. 2023"Do Anything Now": Characterizing and Evaluating In-The-Wild Jailbreak Prompts on Large Language Modelscited by 489
  3. 2024When Can LLMs Actually Correct Their Own Mistakes? A Critical Survey of Self-Correction of LLMscited by 240
  4. 2023Generative artificial intelligence enhances creativity but reduces the diversity of novel contentcited by 167
  5. 2023Should ChatGPT be Biased? Challenges and Risks of Bias in Large Language Modelscited by 148
  6. 2023Unveiling Security, Privacy, and Ethical Concerns of ChatGPTcited by 53
  7. 2025Large Language Models Pass the Turing Testcited by 48
  8. 2024Parameter-Efficient Fine-Tuning in Large Models: A Survey of Methodologiescited by 47
  9. 2024The Value, Benefits, and Concerns of Generative AI-Powered Assistance in Writingcited by 37
  10. 2024Security of AI Agentscited by 26
  11. 2020The Pace of Artificial Intelligence Innovations: Speed, Talent, and Trial-and-Errorcited by 13
  12. 2024Generative Discrimination: What Happens When Generative AI Exhibits Bias, and What Can Be Done About Itcited by 7
2 more
  1. 2024The Cognitive Capabilities of Generative AI: A Comparative Analysis with Human Benchmarkscited by 6
  2. 2023Dissecting Bias of ChatGPT in College Major Recommendationscited by 3

Built on by

Not cited yet.