用稀疏低秩适配提升大模型不确定性估计，更可信。
Bayesian Sparse Low-Rank Adaptation for Large Language Model Uncertainty Estimation
- 在低秩适配的秩维度上做随机掩码，实现贝叶斯正则化。
- 实验显示模型校准度显著提升，推理准确率不变。
- 适合需要可信输出的场景，如医疗、金融决策系统。
大语言模型虽具备出色推理能力，但任务微调时常表现出过度自信，严重制约其可信部署。我们提出数据自适应低秩适配（DALorRA），一种简单有效的变分贝叶斯稀疏框架，将不确定性量化范式从密集参数空间转移到低秩适配（LoRA）的轻量级秩层面。基于LoRA本质上由多个秩一组件构成、可能带来冗余模型容量的洞察，DALorRA对秩维度施加随机掩码，实现训练时的贝叶斯正则化与推理时的集成式校准。大量实验证明，该方法在不牺牲推理准确率的前提下，显著提升了大模型的校准性能。
原文摘要 · Abstract (English)
Large language models (LLMs) exhibit remarkable reasoning capabilities, but their task-specific fine-tuning is notoriously plagued by overconfidence, severely hindering trustworthy deployment. We propose Data-Adaptive Lower-Rank Adaptation (DALorRA), a simple and effective variational Bayesian sparse framework that shifts the paradigm of uncertainty quantification from the dense parameter space to the lightweight rank level of low-rank adaptation (LoRA). With the insight that LoRA essentially aggregates multiple rank-one components that may provide superfluous model capacity, DALorRA imposes stochastic masking on rank dimensions, enabling Bayesian regularization of model capacity during training and ensemble-like calibration during inference. Extensive experiments demonstrate DALorRA's excellent calibration of LLMs without compromising reasoning accuracy.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。