Papers › Bayesian Low-rank Adaptation for Large Language Models

Bayesian Low-rank Adaptation for Large Language Models

24 Aug 2023arXiv:2308.13111archive 2025-07-28

Adam X. Yang, Maxime Robeyns, Xi Wang, Laurence Aitchison

Low-rank adaptation (LoRA) has emerged as a new paradigm for cost-efficient fine-tuning of large language models (LLMs). However, fine-tuned LLMs often become overconfident especially when fine-tuned on small datasets. Bayesian methods, with their inherent ability to estimate uncertainty, serve as potent tools to mitigate overconfidence and enhance calibration. In this work, we introduce Laplace-LoRA, which applies a Bayesian approach to the LoRA parameters. Specifically, Laplace-LoRA applies a Laplace approximation to the posterior over the LoRA parameters, considerably improving the calibration of fine-tuned LLMs.

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