@inproceedings{majumdar-etal-2025-genetic,
    title = "Genetic Instruct: Scaling up Synthetic Generation of Coding Instructions for Large Language Models",
    author = "Majumdar, Somshubra  and
      Noroozi, Vahid  and
      Samadi, Mehrzad  and
      Narenthiran, Sean  and
      Ficek, Aleksander  and
      Ahmad, Wasi Uddin  and
      Huang, Jocelyn  and
      Balam, Jagadeesh  and
      Ginsburg, Boris",
    editor = "Rehm, Georg  and
      Li, Yunyao",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/acl25-workshop-ingestion/2025.acl-industry.16/",
    pages = "208--221",
    ISBN = "979-8-89176-288-6",
    abstract = "Large Language Models (LLMs) require high quality instruction data for effective alignment, particularly in code generation tasks where expert curated datasets are expensive to produce. We present Genetic-Instruct, a scalable algorithm for synthesizing large-scale, high quality coding instructions using evolutionary principles. Starting from a small set of seed instructions, Genetic-Instruct generates diverse and challenging instruction-code pairs by leveraging an Instructor-LLM for generation, a Coder-LLM for code synthesis, and a Judge-LLM for automatic quality evaluation. Our proposed approach is highly parallelizable and effective even with a small seed data and weaker generator models. We generated more than 7.5 million coding instructions with the proposed approach. Then we evaluated it by fine-tuning LLMs with the synthetic samples and demonstrated a significant improvement in their code generation capability compared to the other synthetic generation approaches and publicly available datasets. Our results highlight the efficiency, scalability, and generalizability of the Genetic-Instruct framework."
}