arXiv AI July 28, 2026 By Moule Lin, Shuhao Guan, Andrea Patane, David Gregg, Goetz Botterweck

Bayesian-LoRA: Probabilistic Low-Rank Adaptation of Large Language Models

arXiv:2601. 21003v3 Announce Type: replace Abstract: Large Language Models usually put more emphasis on accuracy and therefore, will guess even when not certain about the prediction, which is especially severe when fine-tuned on small datasets due to the inherent tendency toward miscalibration.

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arXiv Machine Learning

Jul 3

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

arXiv:2607. 02182v1 Announce Type: new Abstract: Large language models (LLMs) exhibit remarkable reasoning capabilities, but their task-specific fine-tuning is notoriously plagued by overconfidence, severely hindering trustworthy deployment.

By Jijie Zhang, Zhe Ren, Quan Zhang, Dandan Guo

llms fine-tuning

arXiv Machine Learning

Jun 30

BaRA: Bayesian Adaptive Rank Allocation for Parameter-Efficient Fine-Tuning

arXiv:2606. 29184v1 Announce Type: new Abstract: While Low-rank adaptation (LoRA) enables highly efficient fine-tuning by constraining task-specific updates to fixed low-rank subspaces, this rigid design limits representational flexibility and often results in overconfident predictions and miscalibrated uncertainty, especially in low-data regimes.

By Zhibin Duan, Yuhong Wang, Jiahong Fu, Zongsheng Yue, Bo Chen, Zongben Xu

fine-tuning benchmarks

arXiv AI

Aug 5

FraQ: Efficient Coordinate-Space Recompression for Federated Low-Rank Adaptation

arXiv:2608. 03605v1 Announce Type: new Abstract: Federated fine-tuning with Low-Rank Adaptation (LoRA) enables efficient collaborative adaptation of Large Language Models (LLMs) without centralizing private data.

By Shenghui Li, Thiemo Voigt

llms nlp fine-tuning benchmarks

arXiv Machine Learning

Jun 2

RefLoRA: Refactored Low-Rank Adaptation for Efficient Fine-Tuning of Large Models

arXiv:2505. 18877v4 Announce Type: replace Abstract: Low-Rank Adaptation (LoRA) lowers the computational and memory overhead of fine-tuning large models by updating a low-dimensional subspace of the pre-trained weight matrix.

By Yilang Zhang, Bingcong Li, Georgios B. Giannakis

llms fine-tuning benchmarks

arXiv AI

Jul 21

SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling

arXiv:2607. 16252v1 Announce Type: cross Abstract: Low-Rank Adaptation (LoRA) is a widely used parameter-efficient fine-tuning (PEFT) method for large language models.

By Yupeng Chang, Yuan Wu, Yi Chang

llms fine-tuning benchmarks

arXiv AI

Jun 15

Beyond LoRA: Is Sparsity-Induced Adaptation Better?

arXiv:2606. 13767v1 Announce Type: cross Abstract: Low-rank adaptation (LoRA) and its variants provide a memory- and compute-efficient alternative to full fine-tuning of pre-trained models.

By Elijah Cadenhead, Cristian McGee, Xin Li, El Houcine Bergou, Aritra Dutta

fine-tuning efficiency

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Related stories

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

BaRA: Bayesian Adaptive Rank Allocation for Parameter-Efficient Fine-Tuning

FraQ: Efficient Coordinate-Space Recompression for Federated Low-Rank Adaptation

RefLoRA: Refactored Low-Rank Adaptation for Efficient Fine-Tuning of Large Models

SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling

Beyond LoRA: Is Sparsity-Induced Adaptation Better?