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arXiv · 2411.00005

Mastering the Craft of Data Synthesis for CodeLLMs

Meng Chen·Philip Arthur·Qianyu Feng·Cong Duy Vu Hoang·Yu-Heng Hong·Mahdi Kazemi Moghaddam·Omid Nezami·Thien Nguyen·Gioacchino Tangari·Duy Vu·Thanh Vu·Mark Johnson·Krishnaram Kenthapadi·Don Dharmasiri·Long Duong·Yuan-Fang Li
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Abstract

Large language models (LLMs) have shown impressive performance in \emph{code} understanding and generation, making coding tasks a key focus for researchers due to their practical applications and value as a testbed for LLM evaluation. Data synthesis and filtering techniques have been widely adopted and shown to be highly effective in this context. In this paper, we present a focused survey and taxonomy of these techniques, emphasizing recent advancements. We highlight key challenges, explore future research directions, and offer practical guidance for new researchers entering the field.

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Meng Chen, Philip Arthur, Qianyu Feng, Cong Duy Vu Hoang, Yu-Heng Hong, Mahdi Kazemi Moghaddam, Omid Nezami, Thien Nguyen, Gioacchino Tangari, Duy Vu, Thanh Vu, Mark Johnson, Krishnaram Kenthapadi, Don Dharmasiri, Long Duong, Yuan-Fang Li. 2024-10-16. Mastering the Craft of Data Synthesis for CodeLLMs. https://arxiv.org/abs/2411.00005

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