A Tutorial on the Pretrain-Finetune Paradigm for Natural Language
  Processing
Yu Wang
The pretrain-finetune paradigm represents a transformative approach in
natural language processing (NLP). 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
particularly valuable for quantitative researchers in psychology, offering them
an insightful guide into this innovative approach.