Expert Annotated

Expert annotation of datasets is crucial for advancing various machine learning applications, particularly in domains with complex or nuanced information like law, investigative analysis, and climate science. Current research focuses on creating large, expertly-annotated datasets to train and evaluate models, often employing transformer-based architectures and exploring techniques like instruction tuning and knowledge distillation to improve model accuracy and interpretability. These efforts aim to bridge the gap between human expertise and machine learning capabilities, leading to more reliable and explainable AI systems across diverse fields, improving decision-making and analysis in areas where human judgment is critical. The resulting datasets and improved models serve as valuable benchmarks for the NLP and broader machine learning communities.