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Very large language models (LLMs), such as GPT-3 and Codex have achievedstate-of-the-art performance on several natural-language tasks, and show greatpromise also for code. A particularly exciting aspect of LLMs is their knackfor few-shot and zero-shot learning: they can learn to perform a task with veryfew examples. Few-shotting has particular synergies in software engineering,where there are a lot of phenomena (identifier names, APIs, terminology, codingpatterns) that are known to be highly project-specific. However,project-specific data can be quite limited, especially early in the history ofa project; thus the few-shot learning capacity of LLMs might be very relevant.In this paper, we investigate the use few-shot training with the very large GPT(Generative Pre-trained Transformer) Codex model, and find evidence suggestingthat one can significantly surpass state-of-the-art models forcode-summarization, leveraging project-specific training.

Few-shot training LLMs for project-specific code-summarization