Long Tail

The "long tail" problem in machine learning refers to the challenge of achieving robust performance on rare or under-represented data points, a common occurrence in real-world datasets. Current research focuses on developing methods to improve model accuracy and generalization on these less-frequent data instances, employing techniques like parameter-efficient fine-tuning, mixture-of-experts models, knowledge distillation, and contrastive learning, often integrated with large language models or generative models. Addressing the long tail is crucial for building reliable and fair AI systems across diverse applications, from autonomous driving and medical diagnosis to e-commerce and natural language processing, as it ensures that models perform well not just on common scenarios but also on critical, less frequent ones.