Summary of Aixcoder-7b: a Lightweight and Effective Large Language Model For Code Processing, by Siyuan Jiang et al.
aiXcoder-7B: A Lightweight and Effective Large Language Model for Code Processing
by Siyuan Jiang, Jia Li, He Zong, Huanyu Liu, Hao Zhu, Shukai Hu, Erlu Li, Jiazheng Ding, Yu Han, Wei Ning, Gen Wang, Yihong Dong, Kechi Zhang, Ge Li
First submitted to arxiv on: 17 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed aiXcoder-7B Large Language Model (LLM) is a lightweight and effective solution for code completion tasks. It achieves higher accuracy compared to existing LLMs while having fewer parameters (7 billion). The model’s superiority is attributed to three key factors: multi-objective training with the Structured Fill-In-the-Middle (SFIM) objective, diverse data sampling strategies that consider inter-file relationships, and an extensive high-quality dataset. aiXcoder-7B outperforms six LLMs with similar sizes and even surpasses four larger LLMs in five popular code completion benchmarks and a new benchmark collected by this paper. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary aiXcoder-7B is a special kind of computer model that helps developers write better code. This model is unique because it’s small but very accurate. It was trained on a huge amount of coding data, which allowed it to learn how to understand different types of code. The researchers who created aiXcoder-7B tested it against other similar models and found that it performed better in several tasks. They also shared three important tips for training future models like this one. |
Keywords
» Artificial intelligence » Large language model
