Repository Scale

Repository scale research focuses on developing and evaluating methods for leveraging the vast amounts of data and context within large repositories, addressing limitations of existing systems in handling complex, real-world data. Current research emphasizes the use of large language models (LLMs) and natural language processing (NLP) techniques to improve tasks such as code generation, literature search, and data extraction from diverse sources like images and text corpora. This work is significant because it enables more efficient and effective utilization of large datasets, leading to advancements in various fields including software engineering, AI model training, and academic research.