FaST：面向有限数据的个性化偏好对齐的特征感知采样与调优
FaST: Feature-aware Sampling and Tuning for Personalized Preference Alignment with Limited Data
AI总结：
针对有限数据下的个性化偏好对齐问题，引入DnD与ELIP数据集作为基准，提出参数高效的FaST方法，利用数据自动发现的高级特征实现最优整体性能。
摘要：
由大语言模型（LLM）驱动的对话助手常以一刀切的方式部署，无法适配个体用户偏好。近期，LLM个性化——即定制模型以匹配特定用户偏好——作为缩小这一差距的方法受到越来越多关注。本研究聚焦于每个用户仅能收集少量偏好标注这一实用且具挑战性的场景，将该问题定义为有限数据下的个性化偏好对齐（PPALLI）。为支持该领域研究，我们引入两个数据集——DnD和ELIP，并在其上对多种对齐技术进行基准测试。我们进一步提出FaST，一种高度参数高效的方法，利用从数据中自动发现的高级特征，实现了最佳整体性能。
英文摘要：
LLM-powered conversational assistants are often deployed in a one-size-fits-all manner, which fails to accommodate individual user preferences. Recently, LLM personalization -- tailoring models to align with specific user preferences -- has gained increasing attention as a way to bridge this gap. In this work, we specifically focus on a practical yet challenging setting where only a small set of preference annotations can be collected per user -- a problem we define as Personalized Preference Alignment with Limited Data (PPALLI). To support research in this area, we introduce two datasets -- DnD and ELIP -- and benchmark a variety of alignment techniques on them. We further propose FaST, a highly parameter-efficient approach that leverages high-level features automatically discovered from the data, achieving the best overall performance.
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。
