Core Concepts
Chatbots like GPT can effectively annotate AI research publications with high accuracy, aiding in classification tasks.
Abstract
The article explores the use of chatbot models, specifically GPT, as expert annotators for AI research publications. It addresses the challenges in identifying AI research due to the lack of clear criteria and definitions. By leveraging existing expert labels from arXiv, the study evaluates the performance of GPT models in annotating AI publications. The results show that with effective prompt engineering, chatbots can achieve a 94% accuracy rate in assigning AI labels. Furthermore, training classifiers on GPT-labeled data outperforms those trained on arXiv data by nine percentage points. The study highlights the potential of chatbots as reliable data annotators even in domains requiring subject-area expertise.
Stats
Using prompt engineering, chatbots achieved a 94% accuracy rate in assigning AI labels.
Training classifiers on GPT-labeled data outperformed those trained on arXiv data by nine percentage points.
Quotes
"Chatbots can be effectively used as expert annotators with reliable results."
"GPT models achieved high accuracy rates in labeling AI publications."