Details

Autor(en) / Beteiligte

• Di, Peng
• Li, Jianguo
• Yu, Hang
• Jiang, Wei
• Cai, Wenting
• Cao, Yang
• Chen, Chaoyu
• Chen, Dajun
• Chen, Hongwei
• Chen, Liang
• Fan, Gang
• Gong, Jie
• Gong, Zi
• Hu, Wen
• Guo, Tingting
• Lei, Zhichao
• Li, Ting
• Li, Zheng
• Liang, Ming
• Liao, Cong
• Liu, Bingchang
• Liu, Jiachen
• Liu, Zhiwei
• Lu, Shaojun
• Shen, Min
• Wang, Guangpei
• Wang, Huan
• Wang, Zhi
• Xu, Zhaogui
• Yang, Jiawei
• Ye, Qing
• Zhang, Gehao
• Zhang, Yu
• Zhao, Zelin
• Zheng, Xunjin
• Zhou, Hailian
• Zhu, Lifu
• Zhu, Xianying

Titel

CodeFuse-13B: A Pretrained Multi-lingual Code Large Language Model

Ist Teil von

• 2024 IEEE/ACM 46th International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP), 2024, p.418-429

Ort / Verlag

New York, NY, USA: ACM

Erscheinungsjahr

2024

Quelle

ACM Digital Library

Beschreibungen/Notizen

• 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 2. 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.