Abstract
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. We further show that our method exhibits greater robustness against distribution shift, as reflected in its performance on out-of-distribution tasks.
Abstract (translated)
经过在多个自然语言处理（NLP）基准测试中的广泛测试，我们提出了一种简单的结合低秩适应（LoRA）和高斯随机权重平均（SWAG）的方法，有助于在LLM上进行近似贝叶斯推理。通过在LLMs上进行大量实验，我们证明了这种直观且计算效率高的方法提高了模型的泛化能力和标定。我们还证明了我们的方法对于分布漂移的鲁棒性更大，这反映在其在离散任务上的表现。
URL
https://arxiv.org/abs/2405.03425
