3 accepted papers
Recent advances in Code Large Language Models (CodeLLMs) have primarily focused on open-ended code generation, often overlooking the crucial aspect of code understanding & reasoning. To bridge this gap, we introduce CodeMMLU, a comprehensive multiple-choice benchmark designed to evaluate the depth o…
CodeLLMs are widely used for code generation, yet their ability to handle repository-level dependencies remains underexplored. We introduce RepoExec, a benchmark for evaluating repository-level code generation, focusing on executability, functional correctness, and dependency utilization. Our study…
We present The Vault, an open-source dataset of high quality code-text pairs in multiple programming languages for training large language models to understand and generate code. We propose methods for thoroughly extracting samples that use both rules and deep learning to ensure that they contain hi…