研究人如何把大模型当人看，发现温暖感最影响信任和亲密感。
Anthropomorphism and Trust in Human-Large Language Model interactions
- 通过调节模型的亲和力、能力与共情，测试人类感知差异。
- 亲和力和认知共情显著提升信任与关系亲近感，能力影响除拟人化外所有结果。
- 涉及个人情感的话题让模型更像真人，适合关注人机关系的研究者阅读。
随着大型语言模型（LLMs）日益融入日常生活，人们越来越倾向于将其视为具有类人思维与情感的存在，即拟人化。本研究基于超过2,000次人-大模型交互，调查了人们在不同情境下对大模型的拟人化与信任感知维度。共有115名参与者与在亲和力（友好度）、能力（表现力、连贯性）和共情（认知与情感共情）方面系统变化的聊天机器人互动。结果显示，亲和力与认知共情显著预测所有结果（感知拟人化、信任、相似性、关系亲密感、挫败感、有用性），而能力仅影响除拟人化外的所有结果。情感共情主要预测关系相关感知，但不影响认知性判断。话题子分析表明，在主观、个人相关的议题（如恋爱建议）中，这些效应被放大，使用户对模型产生更强的人格相似感与关系连接，相较客观话题更为显著。研究揭示，亲和力、能力和共情是人类赋予人工智能关系性与认知性感知的关键维度。
原文摘要 · Abstract (English)
With large language models (LLMs) becoming increasingly prevalent in daily life, so too has the tendency to attribute to them human-like minds and emotions, or anthropomorphize them. Here, we investigate dimensions people use to anthropomorphize and attribute trust toward LLMs across more than 2,000 human-LLM interactions. Participants (N=115) engaged with LLM chatbots systematically varied in warmth (friendliness), competence (capability, coherence), and empathy (cognitive and affective). Warmth and cognitive empathy significantly predicted perceptions on all outcomes (perceived anthropomorphism, trust, similarity, relational closeness, frustration, usefulness), while competence predicted all outcomes except for anthropomorphism. Affective empathy primarily predicted perceived relational measures, but did not predict the epistemic outcomes. Topic sub-analyses showed that more subjective, personally relevant topics (e.g., relationship advice) amplified these effects, producing greater human-likeness and relational connection with the LLM than did objective topics. Together, these findings reveal that warmth, competence, and empathy are key dimensions through which people attribute relational and epistemic perceptions to artificial agents.
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