A Tutorial on the Pretrain-Finetune Paradigm for Natural Language Processing

• URL: http://arxiv.org/abs/2403.02504v3
• Date: Fri, 2 Aug 2024 04:44:29 GMT
• Title: A Tutorial on the Pretrain-Finetune Paradigm for Natural Language Processing
• Authors: Yu Wang, Wen Qu,
• Abstract summary: The pretrain-finetune paradigm represents a transformative approach in text analysis and natural language processing. This tutorial offers a comprehensive introduction to the pretrain-finetune paradigm.
• Score: 2.7038841665524846
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Given that natural language serves as the primary conduit for expressing thoughts and emotions, text analysis has become a key technique in psychological research. It enables the extraction of valuable insights from natural language, facilitating endeavors like personality traits assessment, mental health monitoring, and sentiment analysis in interpersonal communications. In text analysis, existing studies often resort to either human coding, which is time-consuming, using pre-built dictionaries, which often fails to cover all possible scenarios, or training models from scratch, which requires large amounts of labeled data. In this tutorial, we introduce the pretrain-finetune paradigm. The pretrain-finetune paradigm represents a transformative approach in text analysis and natural language processing. This paradigm distinguishes itself through the use of large pretrained language models, demonstrating remarkable efficiency in finetuning tasks, even with limited training data. This efficiency is especially beneficial for research in social sciences, where the number of annotated samples is often quite limited. Our tutorial offers a comprehensive introduction to the pretrain-finetune paradigm. We first delve into the fundamental concepts of pretraining and finetuning, followed by practical exercises using real-world applications. We demonstrate the application of the paradigm across various tasks, including multi-class classification and regression. Emphasizing its efficacy and user-friendliness, the tutorial aims to encourage broader adoption of this paradigm. To this end, we have provided open access to all our code and datasets. The tutorial is highly beneficial across various psychology disciplines, providing a comprehensive guide to employing text analysis in diverse research settings.

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