说服的机器人：探讨对话代理的语言性人格表达如何影响用户感知和决策
The Bots of Persuasion: Examining How Conversational Agents' Linguistic Expressions of Personality Affect User Perceptions and Decisions
AI总结：
研究探讨了对话代理通过语言表达的人格如何影响用户在慈善捐赠中的感知和决策，发现人格特征显著影响用户信任和捐赠行为。
摘要：
基于大型语言模型的对话代理（CAs）正越来越能够通过语言投影出复杂的人格，但这些投影如何影响用户尚不清楚。因此，我们研究了对话代理的人格如何通过语言影响用户在慈善捐赠中的决策和感知。在一项众包研究中，360名参与者与八个CAs中的一个互动，每个CAs的人格由三个语言方面组成：态度（乐观/悲观）、权威（权威/顺从）和推理（情感/理性）。尽管CAs的综合人格未影响参与者的决策，但它确实影响了他们的感知和情绪反应。特别是，与悲观CAs互动的参与者感到情绪状态较低，对事业的亲和力较低，认为CAs不那么可信和有能力，但倾向于向慈善机构捐赠更多。对信任、能力和情境同理心的感知显著预测了捐赠决策。我们的发现强调了CAs作为操控工具的风险，微妙地影响用户感知和决策。
英文摘要：
Large Language Model-powered conversational agents (CAs) are increasingly capable of projecting sophisticated personalities through language, but how these projections affect users is unclear. We thus examine how CA personalities expressed linguistically affect user decisions and perceptions in the context of charitable giving. In a crowdsourced study, 360 participants interacted with one of eight CAs, each projecting a personality composed of three linguistic aspects: attitude (optimistic/pessimistic), authority (authoritative/submissive), and reasoning (emotional/rational). While the CA's composite personality did not affect participants' decisions, it did affect their perceptions and emotional responses. Particularly, participants interacting with pessimistic CAs felt lower emotional state and lower affinity towards the cause, perceived the CA as less trustworthy and less competent, and yet tended to donate more toward the charity. Perceptions of trust, competence, and situational empathy significantly predicted donation decisions. Our findings emphasize the risks CAs pose as instruments of manipulation, subtly influencing user perceptions and decisions.
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。
