EdgeWisePersona: 一个用于从自然语言交互中进行端侧用户画像构建的数据集
EdgeWisePersona: A Dataset for On-Device User Profiling from Natural Language Interactions
AI总结：
本文提出EdgeWisePersona数据集与基准，用于评估边缘设备小语言模型从智能家居多轮交互中重构用户画像的能力，实验发现小模型性能显著落后于大模型，为端侧隐私友好的行为建模研究提供测试平台。
摘要：
本文介绍了一个全新的数据集与评估基准，旨在评估并改进可部署在边缘设备上的小语言模型，研究重点是智能家居环境中基于多轮自然语言交互的用户画像构建。该数据集的核心是结构化的用户画像，每个画像由一组例程（routine）定义——即由上下文触发的可重复行为模式，这些模式决定了用户与家庭系统的交互方式。以这些画像为输入，大语言模型（LLM）生成对应的交互会话，模拟用户与设备之间真实、多样且具备上下文感知能力的对话。\n该数据集支持的主要任务是画像重构：仅根据交互历史推断用户的例程与偏好。为评估当前模型在真实场景下完成该任务的能力，我们对多个最先进的紧凑型语言模型进行了基准测试，并将其性能与大基座模型进行对比。结果显示，尽管小模型在画像重构方面展现出一定能力，但在准确捕捉用户行为方面仍远逊于大模型。这一性能差距构成了重大挑战——尤其是因为端侧处理具备关键优势，例如保护用户隐私、降低延迟，且无需依赖云端即可提供个性化体验。通过为在这些约束条件下开发和评估行为建模提供真实、结构化的测试平台，本数据集朝着实现可直接在用户自有设备上学习和适配的智能、尊重隐私的AI系统迈出了关键一步。
英文摘要：
This paper introduces a novel dataset and evaluation benchmark designed to assess and improve small language models deployable on edge devices, with a focus on user profiling from multi-session natural language interactions in smart home environments. At the core of the dataset are structured user profiles, each defined by a set of routines - context-triggered, repeatable patterns of behavior that govern how users interact with their home systems. Using these profiles as input, a large language model (LLM) generates corresponding interaction sessions that simulate realistic, diverse, and context-aware dialogues between users and their devices. The primary task supported by this dataset is profile reconstruction: inferring user routines and preferences solely from interactions history. To assess how well current models can perform this task under realistic conditions, we benchmarked several state-of-the-art compact language models and compared their performance against large foundation models. Our results show that while small models demonstrate some capability in reconstructing profiles, they still fall significantly short of large models in accurately capturing user behavior. This performance gap poses a major challenge - particularly because on-device processing offers critical advantages, such as preserving user privacy, minimizing latency, and enabling personalized experiences without reliance on the cloud. By providing a realistic, structured testbed for developing and evaluating behavioral modeling under these constraints, our dataset represents a key step toward enabling intelligent, privacy-respecting AI systems that learn and adapt directly on user-owned devices.
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。
