用预训练数据增强小样本适应训练，提升效率与效果
Backbone Augmented Training for Adaptations
- 利用骨干模型预训练数据扩充适应数据集
- 在数据稀缺时显著提升个性化与语言生成性能
- 适合资源有限但需高效微调的场景
适配技术可高效训练大型骨干模型，如图像生成的扩散模型和基于Transformer的语言模型。尽管各类适配方法以极少计算开销提升性能，但适配数据有限仍带来训练挑战。为此，本文聚焦于骨干模型预训练所用的海量数据，提出骨干增强训练（BAT）方法，利用骨干数据扩充适配数据集。首先，我们建立并证明两个数学核心命题：一个验证了BAT的有效性，另一个明确了其带来收益的条件。进一步，我们设计满足这些命题的先进数据选择策略，并提出ALBAT算法实现该方法。ALBAT在个人化与语言生成任务中，面对稀缺数据时均能高效提升适配训练效果。
原文摘要 · Abstract (English)
Adaptations facilitate efficient training of large backbone models, including diffusion models for image generation and transformer-based language models. While various adaptation techniques enhance performance with minimal computational resources, limited adaptation data often leads to challenges in training. To address this, we focus on the enormous amount of backbone data used to pre-train the backbone models. We propose Backbone Augmented Training (BAT), a method that leverages backbone data to augment the adaptation dataset. First, we formulate and prove two mathematical key propositions: one establishes the validity of BAT, while the other identifies a condition under which BAT benefits adaptation. Furthermore, we introduce an advanced data selection scheme that satisfies these propositions and present ALBAT algorithm to implement this approach. ALBAT efficiently enhances adaptation training in both personalization and language generation tasks with scarce data.
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