Optimized solutions for multilingual support
Although DeepCoder-14B is primarily optimized for Python, the quality of generation for other languages can be improved by..:
- explicit language declaration: Forces the language to be specified at the beginning of the prompt, e.g.
"用C++实现以下功能："maybe"以下是Java类的需求：" - Example guide: Provide 1-2 code snippets of the language as style references to activate the model's fresh-shot learning capability.
- syntactic constraint: Add compilation directive requirements, such as
"需通过g++ -std=c++20编译"maybe"符合ESLint规范" - Post-processing validation: Check the generated results with language-specific toolchains (e.g. Java's Checkstyle, C++'s Clang-Tidy).
Empirical tests show that after adding language standard constraints to C++/Java, the syntactic correctness can be improved from the base 45% to 78%. For emerging languages such as Rust, it is recommended to use Python to generate prototypes before manually translating them.
This answer comes from the articleDeepCoder-14B-Preview: an open source model that specializes in code generationThe
May not be reproduced without permission:AI productivity tools " How to overcome the performance degradation of DeepCoder-14B when processing non-Python languages?
