Summary Programming Languages Boost Each Other arxiv.org
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This report investigates how programming languages can improve each other in code language models through experiments conducted on eight popular languages, using Python-related data as a seed instruction set evolved with GPT-3.5 to generate instructions for others.
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Key Points
- Programming languages can boost each other during the instruction fine-tuning phase of code large language models.
- Extensive experiments were conducted on eight popular programming languages to investigate their interplay and potential for enhancing multilingual code generation capabilities.
- The CodeAlpaca 20K dataset was used to extract Python-related data as a seed instruction set.
- OpenAI's GPT-3.5 was utilized to evolve these instructions and generate new instructions for different programming languages.
- Correlation analysis was used to explore the relationships between programming languages.
- Training language models with monolingual data can enhance their multilingual code generation capabilities.
- Various research papers and projects related to code generation and programming languages are referenced, including CodeGeeX, StarCoder, Code Llama, Training language models to follow instructions with human feedback, and WizardCoder.
Summaries
40 word summary
This report explores how programming languages can enhance each other in code language models. Experiments were conducted on eight popular languages. Python-related data was used as a seed instruction set, which was evolved using GPT-3.5 to generate instructions for other
171 word summary
This technical report explores whether programming languages can boost each other during the instruction fine-tuning phase of code large language models. The report presents extensive experiments on eight popular programming languages (Python, JavaScript, TypeScript, C, C++, Java, Go, HTML
Researchers used the CodeAlpaca 20K dataset to extract Python-related data, which formed the seed instruction set. They then evolved these instructions using OpenAI's GPT-3.5 to generate new instructions for different programming languages. They adopted
C++ Java Go StarCoder 7B 26.83 24.39 28.57 24.69 25.61 23.17 24.39 C ODE M-Python 38.41 11
The excerpt discusses the interplay between different programming languages and how training code language models (LLMs) on monolingual data can enhance their multilingual code generation capabilities. The authors use correlation analysis to investigate the relationships between programming languages and find that Python
This is a list of references to various research papers and projects related to code generation and programming languages. Some of the mentioned projects include CodeGeeX, StarCoder, Code Llama, Training language models to follow instructions with human feedback, WizardCoder