Negative Example

Negative examples are crucial for training effective machine learning models, particularly in natural language processing, by providing crucial information about what a model *should not* generate or predict. Current research focuses on improving the selection, generation, and utilization of negative examples, exploring techniques like contrastive learning, targeted negative training, and dynamic hard negative mining within various model architectures. These advancements aim to enhance model performance, reduce biases, and improve the efficiency of training processes across diverse applications, including language modeling, knowledge graph embedding, and question answering.