CodeFuse-13B: A Pretrained Multi-lingual Code Large Language Model
This addresses the need for improved multi-lingual code understanding in software engineering, particularly for Chinese-speaking developers, though it is incremental as it builds on existing code LLM paradigms.
The paper tackles the problem of limited effectiveness in existing code large language models for non-English inputs in multi-lingual code tasks by introducing CodeFuse-13B, an open-sourced model that achieves a HumanEval pass@1 score of 37.10% and performs better with Chinese prompts in practical scenarios like code generation and translation.
Code Large Language Models (Code LLMs) have gained significant attention in the industry due to their wide applications in the full lifecycle of software engineering. However, the effectiveness of existing models in understanding non-English inputs for multi-lingual code-related tasks is still far from well studied. This paper introduces CodeFuse-13B, an open-sourced pre-trained code LLM. It is specifically designed for code-related tasks with both English and Chinese prompts and supports over 40 programming languages. CodeFuse achieves its effectiveness by utilizing a high quality pre-training dataset that is carefully filtered by program analyzers and optimized during the training process. Extensive experiments are conducted using real-world usage scenarios, the industry-standard benchmark HumanEval-x, and the specially designed CodeFuseEval for Chinese prompts. To assess the effectiveness of CodeFuse, we actively collected valuable human feedback from the AntGroup's software development process where CodeFuse has been successfully deployed. The results demonstrate that CodeFuse-13B achieves a HumanEval pass@1 score of 37.10%, positioning it as one of the top multi-lingual code LLMs with similar parameter sizes. In practical scenarios, such as code generation, code translation, code comments, and testcase generation, CodeFuse performs better than other models when confronted with Chinese prompts.