Large language models (LLMs) are impressive, but they can be overconfident and poorly calibrated, especially when fine-tuned on smaller datasets. Think of it like a student who aced one practice test and now thinks they'll get a perfect score on the real exam. This overconfidence can be a problem, particularly in areas where trust and reliability are crucial. New research explores a clever technique to address this issue by combining two existing methods: Low-Rank Adaptation (LoRA) and Gaussian Stochastic Weight Averaging (SWAG). LoRA makes fine-tuning large models more efficient by focusing on a smaller set of parameters, like giving that overconfident student a more targeted study guide. SWAG, on the other hand, helps the model understand its own uncertainty, like teaching the student to recognize what they *don't* know. This combination, called SWAG-LoRA, allows for a kind of "approximate Bayesian inference," which helps the model better estimate its confidence in its predictions. The results are promising: SWAG-LoRA improves both the accuracy and the 'calibration' of LLMs, meaning the models are better at knowing when they're likely to be right or wrong. This is especially true in multiple-choice question-answering tasks. Moreover, SWAG-LoRA is more robust when faced with unexpected or 'out-of-distribution' data, similar to how a well-prepared student can adapt to a slightly different exam format. While more research is needed, this simple trick could be a significant step towards making LLMs more trustworthy and reliable for real-world applications.