Papers by Patryk Marszałek
Minimal Ranks, Maximum Confidence: Parameter-efficient Uncertainty Quantification for LoRA (2025.findings-emnlp)
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| Challenge: | Low-Rank Adaptation (LoRA) enables parameter-efficient fine-tuning of large language models . standard LoRA lacks mechanisms for uncertainty quantification, leading to overconfident and poorly calibrated models. |
| Approach: | They propose a parameter-efficient Bayesian LoRA method that decomposes weight updates into low-rank matrices. |
| Outcome: | The proposed method achieves strong performance with improved calibration and generalization while maintaining computational efficiency. |