LoraHub: Efficient Cross-Task Generalization via Dynamic LoRA Composition
Research Area: Compute efficient LMs, Learning algorithms for LMs
Keywords: Parameter Efficient Tuning, Cross-Task Generalization, Model Merging
TL;DR: We present LoraHub, a framework for easily combining multiple LoRA modules to achieve similar performance as in few-shot in-context learning.
Abstract: Low-rank adaptation (LoRA) is often employed to fine-tune large language models (LLMs) for new tasks. This paper investigates LoRA composability for cross-task generalization and introduces LoraHub, a simple framework devised for the purposive assembly of LoRA modules trained on diverse given tasks, with the objective of achieving adaptable performance on unseen tasks. With just a few examples from a new task, LoraHub can fluidly combine multiple LoRA modules, eliminating the need for human expertise and assumptions. Notably, the composition requires neither additional model parameters nor gradients. Empirical results on the Big-Bench Hard benchmark suggest that LoraHub, while not surpassing the performance of in-context learning, offers a notable performance-efficiency trade-off in few-shot scenarios by employing a significantly reduced number of tokens per example during inference. Notably, LoraHub establishes a better upper bound compared to in-context learning when paired with different demonstration examples, demonstrating its potential for future development. Our vision is to establish a platform for LoRA modules, empowering users to share their trained LoRA modules. This collaborative approach facilitates the seamless application of LoRA modules to novel tasks, contributing to an adaptive ecosystem.
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Submission Number: 1104
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