@inproceedings{lee-etal-2025-plex,
    title = "{PLEX}: Adaptive Parameter-Efficient Fine-Tuning for Code {LLM}s using Lottery-Tickets",
    author = "Lee, Jaeseong  and
      Han, Hojae  and
      Kim, Jongyoon  and
      Hwang, Seung-won  and
      Kang, Naun  and
      An, KyungJun  and
      Jang, Sungho",
    editor = "Chen, Weizhu  and
      Yang, Yi  and
      Kachuee, Mohammad  and
      Fu, Xue-Yong",
    booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 3: Industry Track)",
    month = apr,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/Ingest-2025-COMPUTEL/2025.naacl-industry.60/",
    pages = "784--793",
    ISBN = "979-8-89176-194-0",
    abstract = "Fine-tuning large language models (LLMs) for code generation is challenging due to computational costs and the underrepresentation of some programming languages (PLs) in pre-training. We propose PLEX, a lottery-ticket based parameter-efficient fine-tuning (PEFT) method that adapts LLMs to either well-supported and underrepresented PLs.During lottery ticket selection, PLEX employs a dual strategy: for well-represented PLs, it leverages the LLM`s full parametric knowledge by selecting from full layers, while for underrepresented PLs, it narrows the selection scope to dense layers, prioritizing the most influential parameters.Additionally, PLEX-E, a low-rank extension of PLEX, further reduces computational costs by limiting the scope of fine-tuning. On MultiPL-E benchmarks, PLEX achieves state-of-the-art performance among PEFT methods, while PLEX-E maintains competitive results with reduced computational overhead. Both variants demonstrate effective adaptation across diverse programming languages, particularly for those underrepresented in pre-training."
}