Overfitting and Generalization Issues in BERT Fine-Tuning

Analyzing a Model Adaptation Scenario

A research team is adapting a large, pre-trained language model for a highly specialized medical text classification task. They use a small, carefully curated dataset for this adaptation process. After training, they find that the model achieves near-perfect accuracy on the data it was trained on, but performs poorly on a new, unseen set of medical texts. What is the most probable cause of this performance gap?

A startup is developing a sentiment analysis tool for customer reviews of a niche product. They have a limited budget, which restricts them to a relatively small, labeled dataset of 1,000 reviews and modest computational resources. Given these constraints, which of the following fine-tuning strategies for a pre-trained language model offers the most balanced approach to achieve good performance while minimizing the risk of poor generalization?