Related papers: Improving Natural Language Capability of Code Large Language Model

Improving Natural Language Capability of Code Large Language Model

URL: http://arxiv.org/abs/2401.14242v1
Date: Thu, 25 Jan 2024 15:33:20 GMT
Title: Improving Natural Language Capability of Code Large Language Model
Authors: Wei Li and Daoguang Zan and Bei Guan and Ailun Yu and Xiaolin Chen and Yongji Wang
Abstract summary: We propose a novel framework, comprising two modules: AttentionExtractor and AttentionCoder. AttentionExtractor is responsible for extracting key phrases from the user's natural language requirements, and AttentionCoder leverages these extracted phrases to generate target code. To validate the effectiveness of the framework, we craft a new code generation benchmark, called MultiNL-H, covering five natural languages.
Score: 13.639938216171185
License: http://creativecommons.org/licenses/by/4.0/
Abstract: 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.

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