High Quality Data

High-quality data is crucial for training effective machine learning models, particularly large language models (LLMs) and multimodal models. Current research focuses on developing methods for creating, cleaning, and selecting high-quality datasets, including techniques like gamified crowdsourcing, counterfactual explanations for data augmentation, and sophisticated filtering algorithms (e.g., ensemble KenLMs) to remove noise and bias. These efforts aim to improve model performance, robustness, and trustworthiness across various applications, from autonomous driving to medical diagnosis, while addressing challenges posed by imbalanced datasets and the high cost of data annotation.