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In support of open and reproducible research, there has been a rapidlyincreasing number of datasets made available for research. As the availabilityof datasets increases, it becomes more important to have quality metadata fordiscovering and reusing them. Yet, it is a common issue that datasets oftenlack quality metadata due to limited resources for data curation. Meanwhile,technologies such as artificial intelligence and large language models (LLMs)are progressing rapidly. Recently, systems based on these technologies, such asChatGPT, have demonstrated promising capabilities for certain data curationtasks. This paper proposes to leverage LLMs for cost-effective annotation ofsubject metadata through the LLM-based in-context learning. Our method employsGPT-3.5 with prompts designed for annotating subject metadata, demonstratingpromising performance in automatic metadata annotation. However, models basedon in-context learning cannot acquire discipline-specific rules, resulting inlower performance in several categories. This limitation arises from thelimited contextual information available for subject inference. To the best ofour knowledge, we are introducing, for the first time, an in-context learningmethod that harnesses large language models for automated subject metadataannotation.

Utilising a Large Language Model to Annotate Subject Metadata: A Case Study in an Australian National Research Data Catalogue