@inproceedings{
yang2023bayesian,
title={Bayesian low-rank adaptation for large language models},
author={Adam Yang and Maxime Robeyns and Xi Wang and Laurence Aitchison},
booktitle={Socially Responsible Language Modelling Research},
year={2023},
url={https://openreview.net/forum?id=9O6hbiDLU7}
}
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.
Bayesian low-rank adaptation for large language models
Adam Yang Maxime Robeyns Xi Wang Laurence Aitchison