Code large language models (Code LLMs) have demonstrated remarkable
performance in code generation. Nonetheless, most existing works focus on
boosting code LLMs from the perspective of programming capabilities, while
their natural language capabilities receive less attention. To fill this gap,
we thus propose a novel framework, comprising two modules: AttentionExtractor,
which is responsible for extracting key phrases from the user's natural
language requirements, and AttentionCoder, which leverages these extracted
phrases to generate target code to solve the requirement. This framework
pioneers an innovative idea by seamlessly integrating code LLMs with
traditional natural language processing tools. To validate the effectiveness of
the framework, we craft a new code generation benchmark, called MultiNL-H,
covering five natural languages. Extensive experimental results demonstrate the
effectiveness of our proposed framework.

提出了一个新的框架，通过集成传统自然语言处理工具，从自然语言需求文本中提取关键词短语，并生成目标代码以解决需求，从而有效提高代码大型语言模型的性能。通过创建一个新的多自然语言代码生成基准测试集，实验证明了该框架的有效性。