Chain of LoRA: Efficient Fine-tuning of Language Models via Residual Learning
(2401.04151)Abstract
Fine-tuning is the primary methodology for tailoring pre-trained LLMs to specific tasks. As the model's scale and the diversity of tasks expand, parameter-efficient fine-tuning methods are of paramount importance. One of the most widely used family of methods is low-rank adaptation (LoRA) and its variants. LoRA encodes weight update as the product of two low-rank matrices. Despite its advantages, LoRA falls short of full-parameter fine-tuning in terms of generalization error for certain tasks. We introduce Chain of LoRA (COLA), an iterative optimization framework inspired by the Frank-Wolfe algorithm, to bridge the gap between LoRA and full parameter fine-tuning, without incurring additional computational costs or memory overheads. COLA employs a residual learning procedure where it merges learned LoRA modules into the pre-trained language model parameters and re-initilize optimization for new born LoRA modules. We provide theoretical convergence guarantees as well as empirical results to validate the effectiveness of our algorithm. Across various models (OPT and llama-2) and seven benchmarking tasks, we demonstrate that COLA can consistently outperform LoRA without additional computational or memory costs.
Overview
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Chain of LoRA (COLA) is an iterative optimization framework designed for efficient fine-tuning of pre-trained LLMs.
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COLA applies low-rank updates to the weight matrices of language models for minimal adjustments with improved performance.
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The method cycles through tuning LoRA modules, merging changes, and initializing new updates, inspired by the Frank-Wolfe algorithm.
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COLA has been empirically validated to outperform LoRA in benchmarks without extra computational or memory costs, showing up to 6.47% test accuracy gains.
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Future research aims to test COLA with various base optimizers and on more demanding tasks to establish its broader capabilities.
Overview of COLA
Chain of LoRA (COLA) introduces an iterative optimization framework to efficiently fine-tune pre-trained LLMs while striking a balance between computational efficiency and model performance. Advancements in fine-tuning methods are crucial considering the expanding scale of models and the diversity of tasks they are expected to perform. The key to COLA's approach is to apply a series of low-rank updates to the weight matrices of the language model instead of adjusting the full set of parameters.
The Shortcomings of LoRA and COLA's Solution
Typically, parameter-efficient fine-tuning methods like Low-Rank Adaptation (LoRA) focus on minimal modifications to a model's weights. Despite its efficiency, LoRA sometimes lags behind full-parameter tuning in terms of generalization ability. COLA aims to bridge this performance gap by implementing residual learning, which adds sequential low-rank modifications that incrementally improve task-specific performance with theoretical and empirical support.
The Methodology
COLA starts with a pre-trained LLM, upon which it applies these low-rank changes through three primary stages: tuning the LoRA modules, tying a knot (merging changes into the main model), and then initializing new adjustments. This cycle is repeated, effectively building a chain of updates that refine the model's weights without significantly increasing computational costs. The process embodies the essence of the Frank-Wolfe algorithm, an established optimization technique known for its projection-free approach to tackling constrained optimization problems.
Empirical and Theoretical Advancement
Researchers validated COLA's efficiency across different benchmark tasks and demonstrated that it surpasses LoRA's performance without incurring extra computational or memory overhead. The strength of COLA resides not just in practice but also in theory, as the mathematical framework guarantees convergence in nonconvex optimization problems. The experimental results using OPT and llama-2 models highlight COLA's potential, yielding a relative test accuracy gain of up to 6.47% compared to the LoRA baseline on certain tasks.
Future Exploration
Moving forward, the research team is investigating COLA's interaction with different base optimizers and applying the framework to more demanding tasks such as generation and summarization. Their ongoing efforts are poised to further unravel COLA's advantages and constraints, potentially establishing it as a cornerstone technique for the efficient fine-tuning of the ever-growing LLMs.
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