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ChatGPT outperforms crowd workers for text-annotation tasks
Fabrizio Gilardi1, Meysam Alizadeh1, Maël Kubli1
1Department of Political Science, University of Zurich, Zurich 8050, Switzerland.
Summary
ChatGPT significantly outperforms human annotators in text classification tasks like relevance and topic detection. This AI model offers higher accuracy and agreement at a fraction of the cost, revolutionizing natural language processing applications.
Area of Science:
- Natural Language Processing (NLP)
- Artificial Intelligence (AI)
- Machine Learning
Background:
- Manual text annotation is crucial for training NLP classifiers and evaluating models.
- Tasks range from simple relevance to complex frame detection, often performed by crowd workers or trained annotators.
Purpose of the Study:
- To compare the performance of ChatGPT against human annotators for various text annotation tasks.
- To evaluate accuracy, intercoder agreement, and cost-effectiveness of AI-driven annotation.
Main Methods:
- Utilized four datasets of tweets and news articles (n = 6,183).
- Assessed ChatGPT's zero-shot performance on tasks including relevance, stance, topics, and frame detection.
- Compared ChatGPT's results against crowd workers and trained annotators.
Main Results:
- ChatGPT's zero-shot accuracy surpassed crowd workers by an average of 25 percentage points.
- ChatGPT demonstrated superior intercoder agreement compared to both crowd workers and trained annotators.
- The per-annotation cost of ChatGPT was less than $0.003, significantly cheaper than MTurk.
Conclusions:
- Large language models like ChatGPT show immense potential to enhance text classification efficiency.
- AI-driven annotation offers a more accurate, consistent, and cost-effective alternative to manual methods.
- This advancement could revolutionize how NLP applications are developed and evaluated.
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