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| Main Authors: | , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2411.00005 |
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| _version_ | 1866912222980079616 |
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| author | Chen, Meng Arthur, Philip Feng, Qianyu Hoang, Cong Duy Vu Hong, Yu-Heng Moghaddam, Mahdi Kazemi Nezami, Omid Nguyen, Thien Tangari, Gioacchino Vu, Duy Vu, Thanh Johnson, Mark Kenthapadi, Krishnaram Dharmasiri, Don Duong, Long Li, Yuan-Fang |
| author_facet | Chen, Meng Arthur, Philip Feng, Qianyu Hoang, Cong Duy Vu Hong, Yu-Heng Moghaddam, Mahdi Kazemi Nezami, Omid Nguyen, Thien Tangari, Gioacchino Vu, Duy Vu, Thanh Johnson, Mark Kenthapadi, Krishnaram Dharmasiri, Don Duong, Long Li, Yuan-Fang |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_00005 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Mastering the Craft of Data Synthesis for CodeLLMs Chen, Meng Arthur, Philip Feng, Qianyu Hoang, Cong Duy Vu Hong, Yu-Heng Moghaddam, Mahdi Kazemi Nezami, Omid Nguyen, Thien Tangari, Gioacchino Vu, Duy Vu, Thanh Johnson, Mark Kenthapadi, Krishnaram Dharmasiri, Don Duong, Long Li, Yuan-Fang Software Engineering Artificial Intelligence 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. |
| title | Mastering the Craft of Data Synthesis for CodeLLMs |
| topic | Software Engineering Artificial Intelligence |
| url | https://arxiv.org/abs/2411.00005 |