让大模型搜索更像真人：加入社交线索提升信息可信度与使用体验
SoulSeek: Exploring the Use of Social Cues in LLM-based Information Seeking
- 在大模型搜索中融入他人存在、行为等社交线索
- 用户感知的搜索结果更可信，反思性浏览行为增加
- 适合研究人机交互、个性化搜索系统设计者
社交线索（如他人存在、行为或身份）在人类信息获取中起关键作用，帮助判断内容相关性和可信度。然而现有基于大语言模型（LLM）的搜索系统主要依赖语义特征，与自然信息获取中的社会化认知不匹配。为填补这一差距，我们探索将社交线索整合进LLM搜索对用户感知、体验和行为的影响。针对正采用LLM搜索的社交媒体平台，通过设计工作坊、原型系统SoulSeek的开发、被试间实验及混合方法分析，研究结果表明：社交线索能提升用户对搜索结果的感知质量与体验，促进反思性信息行为，并揭示当前LLM搜索的局限。研究提出设计启示：强化社会知识理解、支持个性化线索设置、实现可控交互。
原文摘要 · Abstract (English)
Social cues, which convey others' presence, behaviors, or identities, play a crucial role in human information seeking by helping individuals judge relevance and trustworthiness. However, existing LLM-based search systems primarily rely on semantic features, creating a misalignment with the socialized cognition underlying natural information seeking. To address this gap, we explore how the integration of social cues into LLM-based search influences users' perceptions, experiences, and behaviors. Focusing on social media platforms that are beginning to adopt LLM-based search, we integrate design workshops, the implementation of the prototype system (SoulSeek), a between-subjects study, and mixed-method analyses to examine both outcome- and process-level findings. The workshop informs the prototype's cue-integrated design. The study shows that social cues improve perceived outcomes and experiences, promote reflective information behaviors, and reveal limits of current LLM-based search. We propose design implications emphasizing better social-knowledge understanding, personalized cue settings, and controllable interactions.
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