Amortized Bayesian Meta-Learning for Low-Rank Adaptation of Large Language Models

• URL: http://arxiv.org/abs/2508.14285v1
• Date: Tue, 19 Aug 2025 21:57:59 GMT
• Title: Amortized Bayesian Meta-Learning for Low-Rank Adaptation of Large Language Models
• Authors: Liyi Zhang, Jake Snell, Thomas L. Griffiths,
• Abstract summary: Fine-tuning large language models with low-rank adaptaion (LoRA) is a cost-effective way to incorporate information from a specific dataset.<n>It is often unclear how well the fine-tuned LLM will generalize, i.e., how well it will perform on unseen datasets.<n>We propose Amortized Bayesian Meta-Learning for LoRA (ABMLL) to improve generalization and scales to large models.
• Score: 7.075648770762989
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Fine-tuning large language models (LLMs) with low-rank adaptaion (LoRA) is a cost-effective way to incorporate information from a specific dataset. However, it is often unclear how well the fine-tuned LLM will generalize, i.e., how well it will perform on unseen datasets. Methods have been proposed to improve generalization by optimizing with in-context prompts, or by using meta-learning to fine-tune LLMs. However, these methods are expensive in memory and computation, requiring either long-context prompts or saving copies of parameters and using second-order gradient updates. To address these challenges, we propose Amortized Bayesian Meta-Learning for LoRA (ABMLL). This method builds on amortized Bayesian meta-learning for smaller models, adapting this approach to LLMs while maintaining its computational efficiency. We reframe task-specific and global parameters in the context of LoRA and use a set of new hyperparameters to balance reconstruction accuracy and the fidelity of task-specific parameters to the global ones. ABMLL provides effective generalization and scales to large models such as Llama3-8B. Furthermore, as a result of using a Bayesian framework, ABMLL provides improved uncertainty quantification. We test ABMLL on Unified-QA and CrossFit datasets and find that it outperforms existing methods on these benchmarks in terms of both accuracy and expected calibration error.

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