Information Guided Regularization for Fine-tuning Language Models
<|reference_start|>Information Guided Regularization for Fine-tuning Language Models: The pretraining-fine-tuning paradigm has been the de facto strategy for transfer learning in modern language modeling. With the understanding that task adaptation in LMs is often a function of parameters shared across tasks, we argue that a more surgical approach to regularization needs to exist for smoother transfer learning. Towards this end, we investigate how the pretraining loss landscape is affected by these task-sensitive parameters through an information-theoretic lens. We then leverage the findings from our investigations to devise a novel approach to dropout for improved model regularization and better downstream generalization. This approach, named guided dropout, is both task & architecture agnostic and adds no computational overhead to the fine-tuning process. Through empirical evaluations, we showcase that our approach to regularization yields consistently better performance, even in scenarios of data paucity, compared to standardized baselines.<|reference_end|>
@article{sharma2024information,
    title={Information Guided Regularization for Fine-tuning Language Models},
    author={Mandar Sharma, Nikhil Muralidhar, Shengzhe Xu, Raquib Bin Yousuf,
  Naren Ramakrishnan},
    journal={arXiv preprint arXiv:2406.14005},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.14005},
    primaryClass={cs.CL cs.AI cs.LG}
}