通过自监督数据选择与合成实现端侧大语言模型个性化
Enabling On-Device Large Language Model Personalization with Self-Supervised Data Selection and Synthesis
AI总结：
针对端侧LLM个性化面临的标注稀疏、存储有限问题，提出一种自监督在线选择代表性数据并合成相似样本的框架，在准确率和微调速度上优于基线，是首个端侧LLM个性化框架。
摘要：
大语言模型（LLM）部署在边缘设备上后，理想情况下这些设备能够从用户生成的对话数据中学习，以实时生成针对特定用户的个性化回复。然而，用户生成的数据通常包含敏感和隐私信息，即便不被禁止，将这类数据上传到云端进行标注也并非优选方案。虽然可以通过直接询问用户提供偏好回复的方式在本地获取标注，但为了不影响用户体验，这类标注必须是稀疏的。此外，边缘设备的存储通常十分有限，无法支持使用全部用户生成数据进行大规模微调。在标注稀疏且端侧存储有限的前提下，如何实现端侧LLM个性化仍是一个未解决的问题。本文提出了一种新颖的框架，能够以自监督方式在线选择并存储最具代表性的数据。这类数据内存占用小，且只需不频繁地请求用户标注即可用于进一步微调。为提升微调质量，我们利用LLM生成了多组语义相似的问题文本与预期回复对。实验表明，与普通基线方法相比，所提框架实现了最优的用户特定内容生成能力（准确率）与微调速度（性能）。据我们所知，这是首个端侧LLM个性化框架。
英文摘要：
After a large language model (LLM) is deployed on edge devices, it is desirable for these devices to learn from user-generated conversation data to generate user-specific and personalized responses in real-time. However, user-generated data usually contains sensitive and private information, and uploading such data to the cloud for annotation is not preferred if not prohibited. While it is possible to obtain annotation locally by directly asking users to provide preferred responses, such annotations have to be sparse to not affect user experience. In addition, the storage of edge devices is usually too limited to enable large-scale fine-tuning with full user-generated data. It remains an open question how to enable on-device LLM personalization, considering sparse annotation and limited on-device storage. In this paper, we propose a novel framework to select and store the most representative data online in a self-supervised way. Such data has a small memory footprint and allows infrequent requests of user annotations for further fine-tuning. To enhance fine-tuning quality, multiple semantically similar pairs of question texts and expected responses are generated using the LLM. Our experiments show that the proposed framework achieves the best user-specific content-generating capability (accuracy) and fine-tuning speed (performance) compared with vanilla baselines. To the best of our knowledge, this is the very first on-device LLM personalization framework.
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。
