  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.
