Unlocking Web Histories: Leveraging LLMs and RAG to Transform Discovery in Web Archives : Long Paper - iPRES 2025 - Wellington, New Zealand / Te Whanganui-a-Tara, Aotearoa
Abstract
This paper explores how Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) can be applied to improve access to web archives. These collections are often difficult to navigate due to their complexity and the limitations of traditional search tools. The author examines how RAG can help address concerns around trust and transparency in AI by grounding LLM outputs in external, curated sources. At the University of Victoria Libraries, a custom RAG pipeline was developed to build on tools like WARC-GPT. This pipeline enables natural language querying with source attribution and incorporates optimizations in data preprocessing, chunking strategies, and hardware acceleration. When tested on real-world web archives of cultural significance, it demonstrated improved retrieval accuracy and computational efficiency. The discussion also considers the broader implications for digital preservation in an age of uncertainty, emphasizing the importance of trust, ethics, sustainability, and continued human oversight in AI-powered discovery. The findings suggest that RAG offers a promising way to unlock the value of underused digital heritage collections while upholding the foundational values of research libraries, including long-term preservation, equitable access, and responsible stewardship of historic materials.Details
- Creators
- Corey Davis
- Institutions
- Date
- 2025-09-01 00:00:00 +0000
- Keywords
- digital humanities
- Publication Type
- paper
- License
- http://creativecommons.org/licenses/by-sa/4.0/
- Download
- 369984 bytes