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English(EN) Veterans' Mental Health Crisis Severity Annotation: Do LLMs Agree More Than Human Experts?

大型语言模型在心理健康标注方面表现出高度一致性,但落后于人类专家

一篇新近发表在arXiv上的研究评估了大型语言模型(LLMs)在标注心理健康危机严重程度方面的表现,特别关注退伍军人相关的社交媒体内容。该研究将LLM的标注与八位退伍军人主题专家(SMEs)的标注进行了比较,发现虽然一些LLM在模型间一致性方面优于人类专家,但这并不总是能转化为与人类判断的一致性或可靠检测罕见的高严重程度病例。专有模型通常比开源模型表现更好且更稳定,其中GPT-5.6-Terra表现最佳,而Qwen2.5-7B在识别急性自杀意念方面表现突出。 AI

影响 大型语言模型在扩展心理健康标注方面显示出潜力,但在高风险应用中需要仔细验证。

排序理由 该集群包含一篇学术论文,详细介绍了LLM在特定领域性能的研究结果。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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大型语言模型在心理健康标注方面表现出高度一致性,但落后于人类专家

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该集群包含一篇学术论文,详细介绍了LLM在特定领域性能的研究结果。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · K M Sajjadul Islam, Pranav Yaramalla, Nadiyah Johnson, Mark Flower, Praveen Madiraju, Zeno Franco ·

    退伍军人心理健康危机严重性标注:大型语言模型比人类专家更一致吗?

    arXiv:2611.10048v2 Announce Type: replace Abstract: Large language models (LLMs) offer a scalable approach for annotating mental health and crisis-related social media content, but their reliability in safety-critical tasks remains unclear. This study evaluates LLM-based crisis-s…