Current metadata creation for web archives is time consuming and costly due to reliance on human effort. This paper explores the use of gpt-4o for metadata generation within the Web Archive Singapore, focusing on scalability, efficiency, and cost effectiveness. We processed 112 Web ARChive (WARC) files using data reduction techniques, achieving a notable 99.9% reduction in metadata generation costs. By prompt engineering, we generated titles and abstracts, which were evaluated both intrinsically using Levenshtein Distance and BERTScore, and extrinsically with human cataloguers using McNemar's test. Results indicate that while our method offers significant cost savings and efficiency gains, human curated metadata maintains an edge in quality. The study identifies key challenges including content inaccuracies, hallucinations, and translation issues, suggesting that Large Language Models (LLMs) should serve as complements rather than replacements for human cataloguers. Future work will focus on refining prompts, improving content filtering, and addressing privacy concerns through experimentation with smaller models. This research advances the integration of LLMs in web archiving, offering valuable insights into their current capabilities and outlining directions for future enhancements. The code is available at https://github.com/masamune-prog/warc2summary for further development and use by institutions facing similar challenges.
Open Archives Initiative（OAI）是作为一种促进电子印刷品存储库之间互操作性的实用方法而创建的。虽然审调处的范围已经扩大，但电子版储存库仍占审调处数据提供者的很大一部分。在本文中，我简要介绍了OAI eprint存储库，以及使用OAI元数据收集协议（OAI-PMH）从eprint存储库收集元数据的服务。然后，我讨论了几种情况下，元数据收获可能被用来进一步提高电子印刷档案的效用作为学术交流基础设施的一个组成部分。
AMAQA: A Metadata-based QA Dataset for RAG Systems
Institute for Informatics and Telematics, National Research Council（信息与电信研究院，国家研究院）；Department of Information Engineering, University of Pisa（信息工程系，比萨大学）；Department of Computer Science, University of Pisa（计算机科学系，比萨大学）
From UNDRR Reports to Event Records: Schema-Constrained LLM Extraction of Georeferenced Disasters
ETH Zürich（苏黎世联邦理工学院）；University of Maryland（马里兰大学）；MPI（马克斯·普朗克研究所）；University of Toronto（多伦多大学）；United Nations Office for Disaster Risk Reduction（联合国减少灾害风险办公室）
Predicting LLM Correctness in Prosthodontics Using Metadata and Hallucination Signals
1 Department of Data Science, Monash University Indonesia, Tangerang, Indonesia；2 Independent Researcher；3 Department of Prosthodontics, University of Pittsburgh, Pittsburgh, Pennsylvania；4 Department of Restorative Sciences, University of North Carolina Adams School of Dentistry, Chapel Hill, North Carolina；5 Department of Computer Science, Boston University, Boston, Massachusetts
Standardization of Neuromuscular Reflex Analysis -- Role of Fine-Tuned Vision-Language Model Consortium and OpenAI gpt-oss Reasoning LLM Enabled Decision Support System
Old Dominion University（旧 Dominion 大学）；AnaletIQ；McDonald Army Health Center（麦克唐纳陆军医疗中心）；University of Sri Jayewardenepura（Sri Jayewardenepura 大学）；University of Gdańsk（盖尔范大学）；University of Colombo（科伦坡大学）
Autonomous Structuring of Radiology Reports Across Modalities at Archive Scale Using an Open-Weight Large Language Model
Charité – Berlin University of Medicine（柏林夏里特医学院）；Technical University of Munich（慕尼黑工业大学）；University of Salerno（萨莱诺大学）；Aristotle University of Thessaloniki（亚里士多德大学）；Berlin University of Applied Sciences and Technology（柏林应用技术大学）；Berlin Institute of Health（柏林健康研究院）
