SocialPersona: 基于多模态社交媒体上下文的个性化画像与回复基准测试
SocialPersona: Benchmarking Personalized Profiling and Response with Multimodal Social-Media Context
AI总结：
提出SocialPersona基准，评估多模态大语言模型从社交媒体时间线推断用户偏好并用于对话的能力，实验表明模型在细粒度与近期偏好上表现不足。
摘要：
个性化语言模型助手通常通过记忆能力进行评估：模型能否回忆用户在对话中明确陈述的偏好？更全面的个性化需要更困难的能力——从用户自然留下的多模态痕迹中推断出他们关心什么。我们引入了SocialPersona，一个用于评估多模态大语言模型（MLLMs）能否从纵向社交媒体时间线中恢复揭示偏好并在对话中使用的基准。该基准基于171名日常非促销社交媒体用户的纵向时间线构建，包含文本、图像、时间戳以及跨越七个兴趣领域的2,597个人工验证的偏好标签，将稳定兴趣与近期兴趣区分开来。它支持两个任务：从多模态上下文中构建结构化用户画像，以及生成与推断画像一致的回复。对专有和开源MLLMs的实验表明，模型能够识别广泛的兴趣领域，但在细粒度和近期兴趣上表现下降，并且当必须使用推断画像来个性化对话时，性能进一步下降。结合文本和图像提供互补偏好信号的证据，这些结果表明，稳健的跨模态、长期用户建模仍然是一个关键挑战，而SocialPersona可以帮助衡量和推动助手在推断并依据揭示偏好行动方面的进步。
英文摘要：
Personalized language-model assistants are often evaluated through a memory lens: can a model recall preferences users have explicitly stated in dialogue? More comprehensive personalization demands a harder capability -- inferring what users care about from the multimodal traces they naturally leave behind. We introduce SocialPersona, a benchmark for evaluating whether multimodal large language models (MLLMs) can recover revealed preferences from longitudinal social-media timelines and use them in dialogue. Built from longitudinal timelines of 171 everyday, non-promotional social-media users, SocialPersona contains text, images, timestamps, and 2,597 human-verified preference tags across seven interest domains, separating stable interests from recent interests. It supports two tasks: constructing structured user profiles from multimodal context and generating responses aligned with inferred profiles. Experiments with proprietary and open-weight MLLMs show that models can identify broad interest domains, yet their performance drops on fine-grained and recent interests and degrades further when inferred profiles must be used to personalize dialogue. Together with evidence that text and images provide complementary preference signals, these results indicate that robust cross-modal, long-horizon user modeling remains a key challenge, and that SocialPersona can help measure and advance progress toward assistants that infer and act on revealed preferences.
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。
