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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.

Proceedings of the National Academy of Sciences of the United States of America
|July 18, 2023
PubMed
Summary
This summary is machine-generated.

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.

Keywords:
ChatGPThuman annotationslarge language modelstext as datatext classification

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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.