基于语义范围的LLM企业代码仓库自动化定制
Automated Customization of LLMs for Enterprise Code Repositories Using Semantic Scopes
AI总结：
本文提出基于语义范围的LLM企业代码仓库自动化定制方法，通过RAG和FT策略提升代码生成精度，提高开发者生产力。
摘要：
代码补全（CC）是开发人员在与基于LLM的编程助手协作时经常使用的任务。尽管LLM在公开基准测试中性能有所提升，但开箱即用的LLM在生成与模型训练数据未见过的私人代码仓库相匹配的代码时仍面临困难。将代码LLM定制到私人仓库可以提高模型性能。在本文中，我们提出了基于代码中语义范围的自动化LLM定制方法。我们评估了LLM在两个私人企业代码仓库上的真实行业案例，采用两种定制策略：检索增强生成（RAG）和监督微调（FT）。我们的机制用于摄入仓库的数据并形成带有语义范围的训练数据对，帮助模型学习特定仓库的底层模式，为开发者提供更精确的代码，从而提高他们的生产力。中等大小的定制模型的代码补全性能可以显著优于更大容量但未定制的模型。我们还对两个公开基准测试进行了定制分析，并提出了未来工作的机会。
英文摘要：
Code completion (CC) is a task frequently used by developers when working in collaboration with LLM-based programming assistants. Despite the increased performance of LLMs on public benchmarks, out of the box LLMs still have a hard time generating code that aligns with a private code repository not previously seen by the model's training data. Customizing code LLMs to a private repository provides a way to improve the model performance. In this paper we present our approach for automated LLM customization based on semantic scopes in the code. We evaluate LLMs on real industry cases with two private enterprise code repositories with two customization strategies: Retrieval-Augmented Generation (RAG) and supervised Fine-Tuning (FT). Our mechanism for ingesting the repository's data and formulating the training data pairs with semantic scopes helps models to learn the underlying patterns specific to the repository, providing more precise code to developers and helping to boost their productivity. The code completions of moderately sized customized models can be significantly better than those of uncustomized models of much larger capacity. We also include an analysis of customization on two public benchmarks and present opportunities for future work.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
