Cross File Context

Cross-file context, encompassing information spanning multiple files or documents, is crucial for improving the performance of various machine learning tasks. Current research focuses on integrating this context into models, often using transformer architectures, to enhance tasks such as code completion, source code summarization, and machine translation. This involves developing methods to effectively identify and incorporate relevant cross-file information, addressing challenges like limited context length and noisy data. Improved utilization of cross-file context promises significant advancements in software development tools and natural language processing applications.