对比AI与真人对心理健康建议的回应，发现AI表现更优且更温暖。
When AI Gives Advice: Evaluating AI and Human Responses to Online Advice-Seeking for Well-Being
- 用专家评分比较Reddit热门回复与LLM生成建议
- GPT-4o在有效性、温度等维度全面优于GPT-5
- 人类建议可微调后媲美AI，适合融合人机协作场景
寻求建议是核心人类行为，互联网已两次重塑这一过程：先是通过论坛和问答社区实现公众意见众包，如今则由大语言模型（LLMs）推动。然而，这些模型在日常心理健康建议中的质量尚不明确。我们开展了两项研究（共210名参与者），让专家评估高票Reddit回复与LLM生成建议。结果显示，LLM整体评分更高，在有效性、温暖度及再次求助意愿上均显著领先。GPT-4o在除奉承性外所有指标上优于GPT-5，表明性能提升未必带来建议质量改善。第二项研究探讨了人类与算法建议的结合方式，发现人类建议经无感润色后可媲美AI生成内容。研究提出面向融合AI、群体智慧与专家监督的建议系统的设计启示。
原文摘要 · Abstract (English)
Seeking advice is a core human behavior that the internet has reinvented twice: first through forums and Q&A communities that crowdsource public guidance, and now through large language models (LLMs). Yet the quality of this LLM advice for everyday well-being scenarios remains unclear. How does it compare, not only against human comments, but against the wisdom of the online crowd? We ran two studies (N=210) in which experts compared top-voted Reddit advice with LLM-generated advice. LLMs ranked significantly higher overall and on effectiveness, warmth, and willingness to seek advice again. GPT-4o beat GPT-5 on all metrics except sycophancy, suggesting that benchmark gains need not improve advice-giving. In Study-2, we examined how human and algorithmic advice could be combined, and found that human advice can be unobtrusively polished to compete with AI-generated comments. We conclude with design implications for advice-giving agents and ecosystems blending AI, crowd input, and expert oversight.
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