AI on AI: Exploring the Utility of GPT as an Expert Annotator of AI Publications
<|reference_start|>AI on AI: Exploring the Utility of GPT as an Expert Annotator of AI Publications: 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.<|reference_end|>
@article{toney-wails2024ai,
    title={AI on AI: Exploring the Utility of GPT as an Expert Annotator of AI
  Publications},
    author={Autumn Toney-Wails, Christian Schoeberl, James Dunham},
    journal={arXiv preprint arXiv:2403.09097},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.09097},
    primaryClass={cs.CL}
}