大模型能像人一样说服你改变观点，且效果相当。
Can (A)I Change Your Mind?
- 用希伯来语在真实对话中测试大模型说服力
- 大模型和真人说服效果相近，观点改变显著
- 适合关注AI社会影响的研究者与政策制定者
大型语言模型（LLM）驱动的对话代理日益融入日常生活，引发其对人类认知与社会观念影响的关切。尽管以往研究显示LLM可生成有说服力的内容，但多限于受控的英文环境。本研究为预注册实验，在希伯来语环境中，通过200名参与者，考察了静态文本与动态聊天（Telegram）两种交互模式下，LLM与真人对争议性公共政策议题的说服效果。结果表明，无论对话对象是大模型还是真人，亦或交互形式如何，参与者观点转变均显著，且多数情境下信心水平明显提升。这证明了基于大模型的代理在多种场景下具备强大的说服能力，可能深刻影响公众意见形成。
原文摘要 · Abstract (English)
The increasing integration of large language models (LLMs) based conversational agents into everyday life raises critical cognitive and social questions about their potential to influence human opinions. Although previous studies have shown that LLM-based agents can generate persuasive content, these typically involve controlled English-language settings. Addressing this, our preregistered study explored LLMs' persuasive capabilities in more ecological, unconstrained scenarios, examining both static (written paragraphs) and dynamic (conversations via Telegram) interaction types. Conducted entirely in Hebrew with 200 participants, the study assessed the persuasive effects of both LLM and human interlocutors on controversial civil policy topics. Results indicated that participants adopted LLM and human perspectives similarly, with significant opinion changes evident across all conditions, regardless of interlocutor type or interaction mode. Confidence levels increased significantly in most scenarios. These findings demonstrate LLM-based agents' robust persuasive capabilities across diverse sources and settings, highlighting their potential impact on shaping public opinions.
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