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Alessio Buscemi, software engineer at Lifeware SA, discusses the impact of ChatGPT on software engineers and the efficiency of code generation. He presents a comparative study on code generation across 10 programming languages using ChatGPT 3.5, highlighting unexpected results. The performance of different programming languages is analyzed, with discussions on language popularity and implications on industry practices. Alessio also shares insights on current projects, including sentiment analysis and investigating plagiarism.
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Quick takeaways
- Large language models (LLMs) perform better in higher-level programming languages and struggle with lower-level languages due to increased complexity and the absence of automated tools.
- LLMs perform well even in relatively unpopular languages like Julia, challenging the notion that the volume of available examples on the internet determines LLM performance and raising questions about how we evaluate the suitability of languages for code generation through LLMs.
Deep dives
Performance Variation Across Languages
The study observed significant variations in the performance of large language models (LLMs) in code generation across different programming languages. Python was found to perform the best, followed by Julia, JavaScript, Ruby, NR (R language), Perl, and Smalltalk. Lower-level languages such as C++, C, and Go exhibited lower performance, but still outperformed expectations. The results suggest that LLMs tend to perform better in higher-level languages and struggle more with lower-level languages due to increased complexity and the absence of automated tools like garbage collectors.
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