@inproceedings{chen-etal-2025-mastering,
title = "Mastering the Craft of Data Synthesis for {C}ode{LLM}s",
author = "Chen, Meng and
Arthur, Philip and
Feng, Qianyu and
Hoang, Cong Duy Vu and
Hong, Yu-Heng and
Moghaddam, Mahdi Kazemi and
Nezami, Omid and
Nguyen, Duc Thien and
Tangari, Gioacchino and
Vu, Duy and
Vu, Thanh and
Johnson, Mark and
Kenthapadi, Krishnaram and
Dharmasiri, Don and
Duong, Long and
Li, Yuan-Fang",
editor = "Chiruzzo, Luis and
Ritter, Alan and
Wang, Lu",
booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
month = apr,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.naacl-long.620/",
doi = "10.18653/v1/2025.naacl-long.620",
pages = "12484--12500",
ISBN = "979-8-89176-189-6",
abstract = "Large language models (LLMs) have shown impressive performance in \textit{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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%0 Conference Proceedings
%T Mastering the Craft of Data Synthesis for CodeLLMs
%A Chen, Meng
%A Arthur, Philip
%A Feng, Qianyu
%A Hoang, Cong Duy Vu
%A Hong, Yu-Heng
%A Moghaddam, Mahdi Kazemi
%A Nezami, Omid
%A Nguyen, Duc Thien
%A Tangari, Gioacchino
%A Vu, Duy
%A Vu, Thanh
%A Johnson, Mark
%A Kenthapadi, Krishnaram
%A Dharmasiri, Don
%A Duong, Long
%A Li, Yuan-Fang
%Y Chiruzzo, Luis
%Y Ritter, Alan
%Y Wang, Lu
%S Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
%D 2025
%8 April
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-189-6
%F chen-etal-2025-mastering
%X Large language models (LLMs) have shown impressive performance in 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.
%R 10.18653/v1/2025.naacl-long.620
%U https://aclanthology.org/2025.naacl-long.620/
%U https://doi.org/10.18653/v1/2025.naacl-long.620
%P 12484-12500
Markdown (Informal)
[Mastering the Craft of Data Synthesis for CodeLLMs](https://aclanthology.org/2025.naacl-long.620/) (Chen et al., NAACL 2025)
ACL
- Meng Chen, Philip Arthur, Qianyu Feng, Cong Duy Vu Hoang, Yu-Heng Hong, Mahdi Kazemi Moghaddam, Omid Nezami, Duc Thien Nguyen, Gioacchino Tangari, Duy Vu, Thanh Vu, Mark Johnson, Krishnaram Kenthapadi, Don Dharmasiri, Long Duong, and Yuan-Fang Li. 2025. Mastering the Craft of Data Synthesis for CodeLLMs. In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 12484–12500, Albuquerque, New Mexico. Association for Computational Linguistics.