Bayesian Low-rank Adaptation for Large Language Models

• URL: http://arxiv.org/abs/2308.13111v5
• Date: Mon, 5 Feb 2024 21:16:52 GMT
• Title: Bayesian Low-rank Adaptation for Large Language Models
• Authors: Adam X. Yang, Maxime Robeyns, Xi Wang, Laurence Aitchison
• Abstract summary: Low-rank adaptation (LoRA) has emerged as a new paradigm for cost-efficient fine-tuning of large language models (LLMs) We introduce Laplace-LoRA, which applies a Bayesian approach to the LoRA parameters.
• Score: 28.86048553596652
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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