ChatGPT Outperforms Crowd-Workers for Text-Annotation Tasks
This research addresses the efficiency and cost challenges in text annotation for NLP applications, showing potential for broad impact, though it is incremental as it builds on existing large language model capabilities.
The study tackled the problem of manual text annotation for NLP tasks by comparing ChatGPT to crowd-workers and trained annotators on 2,382 tweets, finding that ChatGPT outperformed crowd-workers in accuracy for four out of five tasks, had higher intercoder agreement, and was about twenty times cheaper at less than $0.003 per annotation.
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.