Bayesian Sparse Low-Rank Adaptation for Large Language Model Uncertainty Estimation
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 s
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- Linked via arxiv authorJijie Zhang →
Bayesian Sparse Low-Rank Adaptation for Large Language Model Uncertainty Estimation
- Linked via arxiv authorZhe Ren →
Bayesian Sparse Low-Rank Adaptation for Large Language Model Uncertainty Estimation
- Linked via arxiv authorQuan Zhang →
Bayesian Sparse Low-Rank Adaptation for Large Language Model Uncertainty Estimation
- Linked via arxiv authorDandan Guo →
Bayesian Sparse Low-Rank Adaptation for Large Language Model Uncertainty Estimation