Astro-NER -- Astronomy Named Entity Recognition: Is GPT a Good Domain Expert Annotator?
This addresses data scarcity for NER in scholarly domains like astronomy, offering a collaborative annotation method that is incremental in improving efficiency.
The study tackled the challenge of scarce labeled data for Named Entity Recognition (NER) in astronomy by using a fine-tuned LLM to assist non-domain experts in annotating scientific entities, resulting in moderate agreement with a domain expert and the release of a 5,000-article dataset.
In this study, we address one of the challenges of developing NER models for scholarly domains, namely the scarcity of suitable labeled data. We experiment with an approach using predictions from a fine-tuned LLM model to aid non-domain experts in annotating scientific entities within astronomy literature, with the goal of uncovering whether such a collaborative process can approximate domain expertise. Our results reveal moderate agreement between a domain expert and the LLM-assisted non-experts, as well as fair agreement between the domain expert and the LLM model's predictions. In an additional experiment, we compare the performance of finetuned and default LLMs on this task. We have also introduced a specialized scientific entity annotation scheme for astronomy, validated by a domain expert. Our approach adopts a scholarly research contribution-centric perspective, focusing exclusively on scientific entities relevant to the research theme. The resultant dataset, containing 5,000 annotated astronomy article titles, is made publicly available.