ChatGPT Outperforms Crowd-Workers for Text-Annotation Tasks

• URL: http://arxiv.org/abs/2303.15056v2
• Date: Wed, 19 Jul 2023 14:10:55 GMT
• Title: ChatGPT Outperforms Crowd-Workers for Text-Annotation Tasks
• Authors: Fabrizio Gilardi, Meysam Alizadeh, Ma\"el Kubli
• Abstract summary: We show that ChatGPT outperforms crowd-workers for several annotation tasks. The per-annotation cost of ChatGPT is less than $0.003 -- about twenty times cheaper than MTurk.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Many NLP applications require manual data annotations for a variety of tasks, notably to train classifiers or evaluate the performance of unsupervised models. Depending on the size and degree of complexity, the tasks may be conducted by crowd-workers on platforms such as MTurk as well as trained annotators, such as research assistants. Using a sample of 2,382 tweets, we demonstrate that ChatGPT outperforms crowd-workers for several annotation tasks, including relevance, stance, topics, and frames detection. Specifically, the zero-shot accuracy of ChatGPT exceeds that of crowd-workers for four out of five tasks, while ChatGPT's intercoder agreement exceeds that of both crowd-workers and trained annotators for all tasks. Moreover, the per-annotation cost of ChatGPT is less than $0.003 -- about twenty times cheaper than MTurk. These results show the potential of large language models to drastically increase the efficiency of text classification.

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