Bayesian Low-rank Adaptation for Large Language Models

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

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arXiv · 2308.13111

Bayesian Low-rank Adaptation for Large Language Models

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

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Abstract

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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BibTeX RIS

Adam X. Yang, Maxime Robeyns, Xi Wang, Laurence Aitchison. 2023-08-24. Bayesian Low-rank Adaptation for Large Language Models. https://arxiv.org/abs/2308.13111

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