人格在对话式信息检索中的作用
Role of Personality in Conversational Information Seeking
AI总结：
本研究以大五人格为变量，开展受控被试内实验，发现助手人格与任务的交互作用影响对话式信息检索的信任与决策，助手人格需作为语境敏感的交互设计变量。
AI中文摘要：
大型语言模型（LLMs）正越来越多地被用于信息检索场景，用户可通过对话完成信息查找、比较与评估。在此场景中，助手的作用不止于检索或生成内容，它还会影响用户如何明确约束条件、提出跟进问题、验证主张，以及判断何时答案足以支撑行动。然而，用户人格、助手人格与任务语境如何共同影响这类交互，目前人们知之甚少。本研究将人格作为对话式信息检索中的可控变量，探究其对用户行为及交互质量的影响。我们开展了一项受控的被试内研究，实验中操纵助手人格与任务类型，同时通过大五人格量表（Big Five）测量参与者的人格。26名参与者分别在三种助手人格条件下完成三项信息检索任务：外向型、尽责型与中性型。任务涵盖探索性旅行规划、对比型智能手机购物，以及对验证要求较高的健康与饮食信息检索。数据包括对话日志、行为轨迹、交互后问卷、退出问卷及大五人格测量结果。助手条件的行为表现存在显著差异：外向型助手生成的对话轮次更长，尽责型助手能引发更高的用户话语占比与更多对话轮次，中性基线助手的表现介于二者之间。最显著的影响是任务与助手的交互作用对信任及决策的影响，且偏好的助手风格因任务而异。未出现全局最优的助手风格，但参与者强烈偏好风格选择或自适应调整。这些发现表明，助手人格应作为一种语境敏感的交互设计变量，而非可全局优化的系统属性。
英文摘要：
Large language models (LLMs) are increasingly used for information seeking, where users find, compare, and evaluate information through dialogue. In this role, the assistant does more than retrieve or generate content: it shapes how users articulate constraints, ask follow-up questions, verify claims, and decide when an answer is sufficient for action. Yet little is known about how user personality, assistant personality, and task context jointly influence these interactions. We examine personality as a controllable variable in conversational information seeking and study its effects on user behaviour and interaction quality. We conducted a controlled within-subject study in which assistant personality and task type were experimentally varied, while participant personality was measured using Big Five scores. Twenty-six participants each completed three information-seeking tasks under three assistant personality conditions: extraverted, conscientious, and neutral. Tasks covered exploratory travel planning, comparative smartphone shopping, and verification-sensitive health and diet information seeking. Data included conversation logs, behavioural traces, post-interaction questionnaires, an exit questionnaire, and Big Five measures. The assistant conditions were behaviourally distinct: the extraverted assistant produced longer turns, the conscientious assistant elicited higher user word share and more turns, and the neutral baseline fell between them. The strongest effect was a task-by-assistant interaction on trust and delegation, with preferred styles varying by task. No global winner emerged, but participants strongly preferred style choice or adaptation. These findings position assistant personality as a context-sensitive interactional design variable rather than a globally optimisable system property.
