Mix-CPT: A Domain Adaptation Framework via Decoupling Knowledge Learning and Format Alignment
<|reference_start|>Mix-CPT: A Domain Adaptation Framework via Decoupling Knowledge Learning and Format Alignment: Adapting general large language models (LLMs) to specialized domains presents great challenges due to varied data distributions. This adaptation typically requires continual pre-training on massive domain-specific corpora to facilitate knowledge memorization, followed by training to apply this knowledge following human instructions and preferences. However, this method may result in inefficient knowledge memorization due to a lack of awareness of knowledge utilization and imposes substantial demands on LLMs to simultaneously learn knowledge utilization and format alignment with limited training samples. To facilitate the domain adaptation of LLM, we revise this process and propose a new domain adaptation framework including domain knowledge learning and general format alignment, called Mix-CPT. Specifically, we first conduct a knowledge mixture continual pre-training that concurrently focuses on knowledge memorization and utilization, allowing for mutual reinforcement. To avoid catastrophic forgetting during the continual pre-training process, we further incorporate a logit swap self-distillation constraint. Subsequently, leveraging the knowledge and capabilities acquired during continual pre-training, we efficiently perform instruction tuning and alignment with a few general training samples to achieve format alignment. Extensive experiments demonstrate that our proposed Mix-CPT framework can simultaneously improve the task-solving capabilities of LLMs on the target and general domains compared to the traditional adaptation methods.<|reference_end|>
@article{jiang2024mix-cpt:,
    title={Mix-CPT: A Domain Adaptation Framework via Decoupling Knowledge Learning
  and Format Alignment},
    author={Jinhao Jiang, Junyi Li, Wayne Xin Zhao, Yang Song, Tao Zhang, Ji-Rong
  Wen},
    journal={arXiv preprint arXiv:2407.10804},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.10804},
    primaryClass={cs.CL}
}