Data Annotation: Collection and Biases in NLP

This lecture focuses on the critical aspects of data collection, annotation, and the biases that can arise in natural language processing (NLP). It begins with a recap of fine-tuning techniques and transitions into the importance of data annotation, highlighting the processes involved and the potential biases that can affect model performance. The instructor discusses the significance of benchmarks in evaluating model performance, emphasizing that benchmarks are often constructed from human-created datasets, which can introduce flaws. The lecture outlines the steps involved in building effective benchmarks, including defining tasks, designing annotation guidelines, and ensuring data quality. The discussion also covers the implications of biases, such as spurious correlations and annotation artifacts, which can lead to models learning shortcuts rather than true understanding. The session concludes with a reflection on the necessity of high-quality data for training robust NLP models and the ongoing challenges in creating reliable evaluation metrics.