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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- PossiblePossibly related (embedding) · 51%thu-pacman/chitu →
- PossiblePossibly related (embedding) · 47%chrisliu298/awesome-llm-unlearning →
- LinkedLinked via arxiv author · 85%Jijie Zhang →
“Bayesian Sparse Low-Rank Adaptation for Large Language Model Uncertainty Estimation”
- LinkedLinked via arxiv author · 85%Zhe Ren →
“Bayesian Sparse Low-Rank Adaptation for Large Language Model Uncertainty Estimation”
- LinkedLinked via arxiv author · 85%Quan Zhang →
“Bayesian Sparse Low-Rank Adaptation for Large Language Model Uncertainty Estimation”
- LinkedLinked via arxiv author · 85%Dandan Guo →
“Bayesian Sparse Low-Rank Adaptation for Large Language Model Uncertainty Estimation”
- FuzzyOverlapping authors or contributors · 62%mudler/LocalAI →
“Shared author/contributor keys: guo”