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.

翻译：寻求建议是人类的核心行为，互联网已对其进行了两次重塑：首先是通过论坛和问答社区实现公众指导的众包，现在则通过大语言模型（LLM）实现。然而，LLM针对日常幸福场景所提供建议的质量仍不明确。它不仅与人类评论相比如何，与在线群体的智慧相比又如何？我们进行了两项研究（N=210），由专家比较Reddit上高票选的人类建议与LLM生成的建议。LLM在总体评分以及有效性、温暖度和再次寻求建议的意愿方面均显著更高。GPT-4o在所有指标上均优于GPT-5，除了谄媚性，这表明基准测试的提升未必能改善建议提供能力。在研究2中，我们探讨了如何结合人类与算法建议，发现人类建议可以经过非侵入性的润色，从而与AI生成的评论相竞争。最后，我们总结了对于融合AI、群体输入和专家监督的建议提供代理及生态系统的设计启示。