MTA: 一种用于个性化大语言模型的融合后再适应框架
MTA: A Merge-then-Adapt Framework for Personalized Large Language Model
AI总结：
MTA通过融合后再适应框架，解决个性化大语言模型在存储成本和数据稀疏性方面的挑战，实现高效动态个性化。
摘要：
个性化大语言模型（PLLMs）旨在使模型输出与个体用户偏好对齐，这是用户导向应用的关键能力。然而，为每个用户单独微调一个模块的常见方法面临两个主要限制：（1）存储成本与用户数量成线性关系，使该方法难以扩展；（2）从头开始微调静态模型通常在数据稀疏的用户上表现不佳。为了解决这些挑战，我们提出了MTA，一种用于PLLMs的融合后再适应框架。MTA包含三个关键阶段。首先，我们通过选择锚点用户并在meta-LoRA模块中预训练元个性化特征来构建共享的元LoRA银行。其次，为了确保可扩展性和在静态模型之外实现动态个性化组合，我们引入了自适应LoRA融合阶段。该阶段检索并动态合并最相关的锚点meta-LoRAs以合成一个用户特定的模块，从而消除用户特定存储的需求，并支持更灵活的个性化。第三，我们提出了一种LoRA堆叠用于少样本个性化阶段，该阶段在合并的LoRA上应用一个额外的超低秩、轻量级LoRA模块。微调此模块可以在少样本设置下实现有效的个性化。在LaMP基准测试中的广泛实验表明，我们的方法在多个任务上均优于现有最先进的方法。
英文摘要：
Personalized Large Language Models (PLLMs) aim to align model outputs with individual user preferences, a crucial capability for user-centric applications. However, the prevalent approach of fine-tuning a separate module for each user faces two major limitations: (1) storage costs scale linearly with the number of users, rendering the method unscalable; and (2) fine-tuning a static model from scratch often yields suboptimal performance for users with sparse data. To address these challenges, we propose MTA, a Merge-then-Adapt framework for PLLMs. MTA comprises three key stages. First, we construct a shared Meta-LoRA Bank by selecting anchor users and pre-training meta-personalization traits within meta-LoRA modules. Second, to ensure scalability and enable dynamic personalization combination beyond static models, we introduce an Adaptive LoRA Fusion stage. This stage retrieves and dynamically merges the most relevant anchor meta-LoRAs to synthesize a user-specific one, thereby eliminating the need for user-specific storage and supporting more flexible personalization. Third, we propose a LoRA Stacking for Few-Shot Personalization stage, which applies an additional ultra-low-rank, lightweight LoRA module on top of the merged LoRA. Fine-tuning this module enables effective personalization under few-shot settings. Extensive experiments on the LaMP benchmark demonstrate that our approach outperforms existing SOTA methods across multiple tasks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
