无需训练即可将低秩适配器转为贝叶斯模型，提升不确定性估计
Training-Free Bayesianization for Low-Rank Adapters of Large Language Models
- 通过搜索最大可接受方差，将已有适配器转为贝叶斯形式
- 在多个数据集上优于现有方法，且无需额外训练
- 适合需要快速部署不确定性估计的LLM应用
大语言模型（LLMs）的响应不确定性估计仍是关键挑战。尽管近期贝叶斯方法通过低秩权重更新有效量化不确定性，但通常需复杂微调或后训练流程。本文提出无需训练的贝叶斯化（TFB），一种简单且理论严谨的框架，可在不进行额外训练的情况下，将已训练的低秩适配器高效转化为贝叶斯形式。TFB 在低秩各向同性高斯分布族中系统搜索权重后验的最大可接受方差。理论分析表明，在温和条件下，该搜索过程等价于KL正则化的变分优化，即广义变分推断。综合实验表明，TFB 在不确定性估计和泛化性能上优于现有方法，同时消除复杂贝叶斯化训练流程。代码将公开于 https://github.com/Wang-ML-Lab/bayesian-peft。
原文摘要 · Abstract (English)
Estimating the uncertainty of responses from Large Language Models (LLMs) remains a critical challenge. While recent Bayesian methods have demonstrated effectiveness in quantifying uncertainty through low-rank weight updates, they typically require complex fine-tuning or post-training procedures. In this paper, we propose Training-Free Bayesianization (TFB), a simple yet theoretically grounded framework that efficiently transforms trained low-rank adapters into Bayesian ones without additional training. TFB systematically searches for the maximally acceptable level of variance in the weight posterior, constrained within a family of low-rank isotropic Gaussian distributions. Our theoretical analysis shows that under mild conditions, this search process is equivalent to KL-regularized variational optimization, a generalized form of variational inference. Through comprehensive experiments, we show that TFB achieves superior uncertainty estimation and generalization compared to existing methods while eliminating the need for complex Bayesianization training procedures. Code will be available at https://github.com/Wang-ML-Lab/bayesian-peft.
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