Bayesian Sparse Low-Rank Adaptation for Large Language Model Uncertainty Estimation

arXiv:2607.02182 · cs.LG, cs.CL · Submitted 2026-07-02 · Read on arXiv

cs.LG, cs.CL

Submitted: 2026-07-02

Updated: 2026-08-31

Terminology

• Calibration — 32× in this paper · explained in 7 episodes
• Fine-Tuning — 32× in this paper · explained in 5 episodes
• Adaptation — 28× in this paper · explained in 1 episode
• Accuracy — 27× in this paper · explained in 1 episode
• Uncertainty Quantification — 18× in this paper · explained in 8 episodes
• Parameter-Efficient Fine-Tuning — 9× in this paper · explained in 2 episodes
• Transformer — 9× in this paper · explained in 2 episodes
• LoRA Adapters — 8× in this paper · explained in 3 episodes
• Benchmark — 6× in this paper · explained in 2 episodes
• Downstream Tasks — 6× in this paper · explained in 2 episodes
• Negative Log-Likelihood — 6× in this paper
• Epistemic Uncertainty — 5× in this paper · explained in 9 episodes
• Sparsity — 5× in this paper · explained in 4 episodes
• Regularization — 4× in this paper · explained in 3 episodes
• Computational overhead — 3× in this paper · explained in 2 episodes
• Expected Calibration Error — 3× in this paper
• Large Language Models (LLMs) — 3× in this paper · explained in 6 episodes
• Low-Rank Adaptation (LoRA) — 3× in this paper · explained in 9 episodes
• Core — 2× in this paper · explained in 2 episodes
• Generalization — 2× in this paper · explained in 13 episodes
• Hallucination — 2× in this paper · explained in 3 episodes
• LoRA Fine-Tuning — 2× in this paper · explained in 3 episodes
• Objective Function — 2× in this paper · explained in 2 episodes
• Pruning — 2× in this paper · explained in 4 episodes
• Ablation — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• Bayesian Inference — 1× in this paper · explained in 5 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• Distribution Shift — 1× in this paper · explained in 5 episodes
• Generative AI — 1× in this paper · explained in 2 episodes
• Interpretability — 1× in this paper · explained in 15 episodes
• Knowledge Distillation — 1× in this paper · explained in 11 episodes
• Kullback-Leibler (KL) Divergence — 1× in this paper · explained in 3 episodes
• Maximum Likelihood Estimation — 1× in this paper
• Monte Carlo dropout — 1× in this paper · explained in 2 episodes
• Monte-Carlo Dropout — 1× in this paper
• Parameter-Efficient Fine-Tuning (PEFT) — 1× in this paper · explained in 4 episodes
• Variational Bayes — 1× in this paper · explained in 2 episodes

Sources

• Calibrating LLMs with Information-Theoretic Evidential Deep Learning
• Toward Efficient Uncertainty in LLMs through Evidential Knowledge Distillation
• Scalable Variational Bayesian Fine-Tuning of LLMs via Orthogonalized Low-Rank Adapters
• AdaLoRA: Adaptive Budget Allocation for Parameter-Efficient Fine-Tuning
• Dynamic Low-Rank Sparse Adaptation for Large Language Models
• Post-Optimization Adaptive Rank Allocation for LoRA
• DR-LoRA: Dynamic Rank LoRA for Fine-Tuning Mixture-of-Experts Models
• Teaching Models to Express Their Uncertainty in Words
• Semantic Uncertainty: Linguistic Invariances for Uncertainty Estimation in Natural Language Generation
• Functional-level Uncertainty Quantification for Calibrated Fine-tuning on LLMs
• Uncertainty as Feature Gaps: Epistemic Uncertainty Quantification of LLMs in Contextual Question-Answering
• Auto-Encoding Variational Bayes
• Categorical Reparameterization with Gumbel-Softmax
• The Llama 3 Herd of Models
• Llama 2: Open Foundation and Fine-Tuned Chat Models
• Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge
• Measuring Massive Multitask Language Understanding
• Uncertainty quantification in fine-tuned LLMs using LoRA ensembles
• LoRA ensembles for large language model fine-tuning

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