Genetic Instruct: Scaling up Synthetic Generation of Coding Instructions for Large Language Models
<|reference_start|>Genetic Instruct: Scaling up Synthetic Generation of Coding Instructions for Large Language Models: Large Language Models (LLMs) rely on instruction samples for alignment, but creating these datasets poses challenges, particularly in expert-dependent tasks like coding, which can be cost-prohibitive. One approach to mitigate these challenges is synthesizing data using another LLM. In this paper, we introduce a scalable method for generating synthetic instructions to enhance the code generation capability of LLMs. The proposed algorithm, Genetic-Instruct, mimics evolutionary processes, utilizing self-instruction to create numerous synthetic samples from a limited number of seeds. Genetic-Instruct is designed for efficient scaling of the generation process. Fine-tuning multiple coding LLMs with the synthetic samples demonstrates a significant improvement in their code generation accuracy compared to the baselines.<|reference_end|>
@article{majumdar2024genetic,
    title={Genetic Instruct: Scaling up Synthetic Generation of Coding Instructions
  for Large Language Models},
    author={Somshubra Majumdar, Vahid Noroozi, Sean Narenthiran, Aleksander Ficek,
  Jagadeesh Balam, Boris Ginsburg},
    journal={arXiv preprint arXiv:2407.21077},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.21077},
    primaryClass={cs.CL cs.LG cs.NE}
}