PERSOMA：面向个性化语言提示的个性化软提示适配器架构
PERSOMA: PERsonalized SOft ProMpt Adapter Architecture for Personalized Language Prompting
AI总结：
PERSOMA提出一种个性化软提示适配器架构，通过将用户交互历史重采样、压缩并转化为软提示嵌入，高效建模用户偏好，实验证明其在处理大规模复杂用户历史上优于现有嵌入和文本提示方法。
摘要：
理解用户大量交互历史中的细微差别，是构建能够适应用户不断变化偏好的准确且个性化自然语言系统的关键。为解决这一问题，我们提出了PERSOMA，即个性化软提示适配器架构。与以往针对大语言模型的个性化提示方法不同，PERSOMA提供了一种高效捕捉用户历史的新方法。它通过将交互重新采样并压缩为自由形式文本，再转化为具有表现力的软提示嵌入来实现这一目标，并建立在近期利用嵌入表示作为大语言模型输入的研究基础之上。我们通过评估多种适配器架构、第一阶段采样策略、LoRA等参数高效微调技术以及其他个性化方法，严格验证了我们的方法。结果表明，与现有的基于嵌入和基于文本提示的技术相比，PERSOMA在处理大量且复杂的用户历史方面具有更优越的能力。
英文摘要：
Understanding the nuances of a user's extensive interaction history is key to building accurate and personalized natural language systems that can adapt to evolving user preferences. To address this, we introduce PERSOMA, Personalized Soft Prompt Adapter architecture. Unlike previous personalized prompting methods for large language models, PERSOMA offers a novel approach to efficiently capture user history. It achieves this by resampling and compressing interactions as free form text into expressive soft prompt embeddings, building upon recent research utilizing embedding representations as input for LLMs. We rigorously validate our approach by evaluating various adapter architectures, first-stage sampling strategies, parameter-efficient tuning techniques like LoRA, and other personalization methods. Our results demonstrate PERSOMA's superior ability to handle large and complex user histories compared to existing embedding-based and text-prompt-based techniques.
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。
