In the world of software engineering, general large language models (LLMs) like ChatGPT have shown great promise in code generation. This has led to the creation of specialized LLMs, called Code LLMs. Many of these are made by fine-tuning general LLMs. They're often updated frequently and their performance depends on the base LLMs. But there's a gap in understanding how these Code LLMs differ from general ones and how well they work across different software tasks. Let's dive into this.

How are Code LLMs different from each other and from general LLMs? Do they really perform better in coding tasks? And which LLMs excel at specific coding challenges? To answer these questions, researchers gathered 134 works from major databases and open-source communities. They looked at who publishes these Code LLMs and how they relate to each other and general LLMs. They also tested how well Code LLMs and general LLMs perform in various coding tasks. Lastly, they used multiple benchmarks to find the best LLMs for each coding task.

This research helps developers pick the right base models for advanced Code LLMs and gives practitioners insight into improving these models. It's a roadmap for understanding where coding AI is headed.

Coding with AI: A Closer Look at Specialized Language Models

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