[2303.15056] ChatGPT Outperforms Crowd-Workers for Text-Annotation Tasks
source link: https://arxiv.org/abs/2303.15056
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[Submitted on 27 Mar 2023]
ChatGPT Outperforms Crowd-Workers for Text-Annotation Tasks
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.
| Subjects: | Computation and Language (cs.CL); Computers and Society (cs.CY) |
| Cite as: | arXiv:2303.15056 [cs.CL] |
| (or arXiv:2303.15056v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2303.15056 |
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