Selection bias

Selection bias refers to systematic bias caused by improper sample selection during the data collection process, which affects the training outcomes and generalization capabilities of natural language processing (NLP) models. The goal is to identify and correct such biases, ensuring that the model learns from a more representative dataset, thereby improving the accuracy and reliability of the model. In NLP, avoiding selection bias is crucial for enhancing model performance and application value.