Pre-Trained ChatGPT Outperforms Data Annotation in Text Classification
Data annotation for text classification can be a time-consuming and expensive process. However, recent advancements in natural language processing have introduced pre-trained models like ChatGPT that show promising results in achieving higher accuracy on text classification tasks. In this article, we explore the effectiveness of pre-trained ChatGPT models compared to traditional data annotation methods.
To begin, we conducted two experiments. Firstly, we made predictions on text data using ChatGPT and compared the results with a test set. Secondly, we utilized ChatGPT to annotate text data and employed the annotated data to train a machine learning model.
The findings of our experiments revealed that directly predicting text labels using ChatGPT outperformed the traditional approach of data annotation followed by model training. This highlights the practical benefits of utilizing pre-trained models like ChatGPT in both data annotation and text classification tasks.
To provide a baseline for comparison, we initially trained a basic machine learning model using a Random Forest classifier on the IMDb dataset, which consists of labeled movie reviews. We employed TF-IDF features to convert the text data into numerical representations and split the dataset into training and testing sets. The accuracy score was used as a metric for sentiment prediction, and the baseline model achieved an accuracy of 65% on the test set.
Next, we incorporated ChatGPT into our text classification process. By directly making predictions on the test set using ChatGPT, we achieved an impressive accuracy of 95%, which was 30% higher than the accuracy of the base model. This demonstrates the significant performance improvement that ChatGPT can bring to text classification tasks.
Moving on to data annotation, we used ChatGPT to assign sentiments to the movie reviews in the training set. We then trained a Random Forest model on the annotated data and evaluated its performance. The model achieved an accuracy of 68.33%, which was 3% better than the accuracy achieved through manual annotations.
In conclusion, pre-trained models like ChatGPT offer a valuable alternative to traditional data annotation methods, particularly when dealing with small datasets. The experiments conducted in this article highlight the superior performance of ChatGPT in text classification tasks and the potential for using it in data annotation. Leveraging ChatGPT can save time and resources while achieving higher accuracy in sentiment analysis and other text classification applications.
