用数据驱动初始化提升低资源场景下LoRA训练效率
$D^2LoRA$: Data-Driven LoRA Initialization for Low Resource Tasks
- 基于任务数据动态初始化LoRA权重，加速收敛
- 在极小数据下提升GSM8K准确率1%、标题生成ROUGE高2点
- 适合数据稀缺时快速适配大模型的场景
在数据稀缺的场景下，微调大语言模型对提升其性能至关重要。传统LoRA方法在小样本条件下收敛速度较慢。本文分析了监督微调（SFT）、直接偏好优化（DPO）和奇数比偏好优化（ORPO）在任务特定学习中的表现，并提出一种数据驱动的LoRA初始化方法 $D^2LoRA$，以提高训练效率。实验对比显示，在极端数据受限条件下，$D^2LoRA$ 在GSM8K基准上提升1%准确率，在标题生成任务中使ROUGE得分提高2点，有效缓解灾难性遗忘，支持大模型在少样本情况下高效适配多任务，显著降低训练成本与数据消耗。
原文摘要 · Abstract (English)
Tuning large language models is essential for optimizing their performance across diverse applications, particularly in scenarios with limited data availability. Tuning large language models in scarce data scenarios is crucial, particularly given that the convergence speed of the LoRA method is lower than that of full fine-tuning. In this paper, we present an analysis of post-training methods including Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), and Odds Ratio Preference Optimization (ORPO) within the context of task-specific learning using the LoRA method. Next we introduce $D^2LoRA$, a data-driven approach for initializing LoRA metrics that enhances training efficiency, especially in limited-data settings. Our experiments compare $D^2LoRA$ with vanilla LoRA in terms of performance and catastrophic forgetting under extremely data-constrained conditions. The results demonstrate that $D^2LoRA$ achieves a 1% improvement GSM8K benchmark and a 2-point improvement in ROUGE score in title generation tasks. $D^2LoRA$ facilitates the adaptation of LLMs to multiple tasks even when task-specific data is scarce, thereby reducing training expenses and offering data cost.
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