Efficient Bayesian Adaptation of Large Language Models Using Gaussian Stochastic Weight Averaging and Low-Rank Adaptation

Core Concepts

A simple combination of Low-Rank Adaptation (LoRA) and Gaussian Stochastic Weight Averaging (SWAG) can effectively enable approximate Bayesian inference in large language models, improving their generalization and calibration.

Abstract

The paper proposes a method that combines Low-Rank Adaptation (LoRA) and Gaussian Stochastic Weight Averaging (SWAG) to enable efficient and effective Bayesian adaptation of large language models (LLMs). Key highlights: LLMs often suffer from overconfidence and poor calibration, especially when fine-tuned on small datasets. LoRA enables parameter-efficient fine-tuning of LLMs by introducing low-rank adaptation matrices, but the resulting models still exhibit poor calibration. The authors integrate SWAG, a simple Bayesian inference method, with LoRA to obtain an approximate Bayesian treatment of the LoRA parameters. Through extensive testing on NLP benchmarks, the authors demonstrate that their SWAG-LoRA approach improves model generalization and calibration compared to standard LoRA fine-tuning, MC Dropout, and LoRA ensembles. The authors also show that their method exhibits greater robustness against distribution shift, outperforming more sophisticated techniques like Laplace-LoRA on out-of-distribution tasks. The key advantages of the proposed method are its simplicity, computational efficiency, and consistent improvements in accuracy and calibration across various datasets.

Stats

Fine-tuning large language models on the full set of weights is inefficient and prohibitively expensive. Large language models often suffer from overconfidence and poor calibration, especially when fine-tuned on small datasets. Low-Rank Adaptation (LoRA) can enable parameter-efficient fine-tuning of large language models, but the resulting models still exhibit poor calibration.

Quotes

"Fine-tuned Large Language Models (LLMs) often suffer from overconfidence and poor calibration, particularly when fine-tuned on small datasets." "We propose a simple combination of Low-Rank Adaptation (LoRA) with Gaussian Stochastic Weight Averaging (SWAG), facilitating approximate Bayesian inference in LLMs." "Through extensive testing across several Natural Language Processing (NLP) benchmarks, we demonstrate that our straightforward and computationally efficient approach improves model generalization and calibration."

Key Insights Distilled From

Gaussian Stochastic Weight Averaging for Bayesian Low-Rank Adaptation of Large Language Models

by Emre... at arxiv.org 05-07-2024

https://arxiv.org/pdf/2405.03425.pdf

Deeper Inquiries

How can the proposed SWAG-LoRA method be extended to enable more expressive Bayesian modeling, such as capturing multimodal or non-Gaussian posterior distributions

The SWAG-LoRA method can be extended to enable more expressive Bayesian modeling by incorporating techniques to capture multimodal or non-Gaussian posterior distributions. One approach is to use a mixture of Gaussians to model the posterior distribution, allowing for multiple modes in the distribution. This can be achieved by sampling from multiple Gaussian distributions and combining them to form a multimodal distribution. Additionally, non-Gaussian distributions can be approximated using more flexible distributions such as the von Mises-Fisher distribution for directional data or the Student's t-distribution for heavy-tailed distributions. By incorporating these techniques, the SWAG-LoRA method can better capture the complexity of the posterior distribution and provide more accurate uncertainty estimates.

What are the potential limitations of the Gaussian assumption in the SWAG-LoRA approach, and how could they be addressed

The Gaussian assumption in the SWAG-LoRA approach may have limitations in capturing the full complexity of the posterior distribution, especially in cases where the true distribution is multimodal or exhibits heavy tails. One potential limitation is that the Gaussian assumption may oversimplify the uncertainty estimates, leading to underestimation of uncertainty in certain regions of the parameter space. To address this limitation, one approach is to use more flexible distributions such as the Laplace distribution or the mixture of Gaussians to better capture the true distribution's characteristics. Additionally, incorporating techniques like variational inference or Hamiltonian Monte Carlo can help improve the accuracy of the posterior approximation and mitigate the limitations of the Gaussian assumption.

How might the SWAG-LoRA method be adapted to handle other types of large neural models beyond language models, such as vision transformers or graph neural networks

The SWAG-LoRA method can be adapted to handle other types of large neural models beyond language models, such as vision transformers or graph neural networks, by modifying the architecture-specific components while retaining the core principles of the SWAG-LoRA approach. For vision transformers, the attention mechanisms and convolutional layers can be integrated with the LoRA adaptation to enable efficient fine-tuning and Bayesian inference. Similarly, for graph neural networks, the message passing and aggregation layers can be adapted to incorporate the LoRA technique for parameter-efficient fine-tuning. By customizing the LoRA adaptation to suit the specific architecture requirements of vision transformers or graph neural networks, the SWAG-LoRA method can be extended to a broader range of neural models while maintaining its effectiveness in improving model generalization and calibration.