Gaussian Stochastic Weight Averaging for Bayesian Low-Rank Adaptation of Large Language Models
<|reference_start|>Gaussian Stochastic Weight Averaging for Bayesian Low-Rank Adaptation of Large Language Models: Fine-tuned Large Language Models (LLMs) often suffer from overconfidence and poor calibration, particularly when fine-tuned on small datasets. To address these challenges, we propose a simple combination of Low-Rank Adaptation (LoRA) with Gaussian Stochastic Weight Averaging (SWAG), facilitating approximate Bayesian inference in LLMs. Through extensive testing across several Natural Language Processing (NLP) benchmarks, we demonstrate that our straightforward and computationally efficient approach improves model generalization and calibration competitively with comparable, more sophisticated methods for Bayesian inference in LLMs. We further show that our method exhibits greater robustness against distribution shift, as reflected in its improved performance on out-of-distribution tasks.<|reference_end|>
@article{onal2024gaussian,
    title={Gaussian Stochastic Weight Averaging for Bayesian Low-Rank Adaptation of
  Large Language Models},
    author={Emre Onal, Klemens Fl"oge, Emma Caldwell, Arsen Sheverdin, Vincent
  Fortuin},
    journal={arXiv preprint arXiv:2405.03425},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.03425},
    primaryClass={cs.CL}
}