Large Language Models (LLMs) have become incredibly popular for their ability to imitate humans and generate unique content. They can answer questions in a way that mimics human responses and even summarize long paragraphs of text, translate languages, and complete code. Recently, there has been a significant increase in the development of LLMs designed specifically for generating code. These code LLMs, such as CodeGeeX, StarCoder, CodeLlama, and Codex, have gained attention in both academic and industrial settings.
To ensure that the instructions aligned with each language’s syntax and requirements, the researchers used two methods: in-depth evolution and in-breadth evolution. In-depth evolution involved starting with a Python-based seed instruction and making it more intricate and tailored to the target language. This method captured the nuances of each specific language. In-breadth evolution, on the other hand, involved creating entirely new instructions specific to HTML, recognizing its distinct nature in web development.
The results of the experiments were remarkable. It was found that different programming languages could have a noticeable impact on the code production abilities of LLMs. For example, when tested on Java code using the HumanEval-X benchmark, a code model called CODEM-Python 15B, which was trained on Python data, showed a significant 17.95% improvement in pass@1 accuracy. This suggests that knowledge of one language, like Python, can greatly enhance code production in another language, such as Java.
Even more astonishingly, when applied to HTML, a markup language, CODEM-HTML 7B showed a significant 15.24% improvement in pass@1 accuracy. This demonstrates that even languages with fundamentally different purposes, like markup languages and conventional programming languages, can improve each other’s code production abilities.
In conclusion, the development of code LLMs has brought exciting opportunities for improving code production. Experimenting with different programming languages has shown that they can complement each other and enhance the performance of code LLMs. This research opens up new possibilities for more efficient and effective code generation.
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