  Identifying scientific publications that are within a dynamic field of
research often requires costly annotation by subject-matter experts. Resources
like widely-accepted classification criteria or field taxonomies are
unavailable for a domain like artificial intelligence (AI), which spans
emerging topics and technologies. We address these challenges by inferring a
functional definition of AI research from existing expert labels, and then
evaluating state-of-the-art chatbot models on the task of expert data
annotation. Using the arXiv publication database as ground-truth, we experiment
with prompt engineering for GPT chatbot models to identify an alternative,
automated expert annotation pipeline that assigns AI labels with 94% accuracy.
For comparison, we fine-tune SPECTER, a transformer language model pre-trained
on scientific publications, that achieves 96% accuracy (only 2% higher than
GPT) on classifying AI publications. Our results indicate that with effective
prompt engineering, chatbots can be used as reliable data annotators even where
subject-area expertise is required. To evaluate the utility of
chatbot-annotated datasets on downstream classification tasks, we train a new
classifier on GPT-labeled data and compare its performance to the arXiv-trained
model. The classifier trained on GPT-labeled data outperforms the arXiv-trained
model by nine percentage points, achieving 82% accuracy.
