Data Deduplication

Data deduplication aims to identify and remove redundant data from large datasets, improving data quality, training efficiency, and model performance. Current research focuses on developing sophisticated techniques, including those leveraging embedding models (like BERT and CLIP), generative models, and locality-sensitive hashing (LSH), to detect both exact and semantic duplicates across diverse data types (text, images, code). These advancements are crucial for various applications, from enhancing the reliability of large language models and improving federated learning to optimizing resource utilization in data storage and retrieval systems. The impact extends to addressing biases in training data and improving the efficiency of machine learning pipelines.