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English(EN) Language-model ratings of depression reflect the rater more than the patient

语言模型在抑郁症评估中显示出高度的评分者依赖性

一篇新近发表在arXiv上的研究调查了语言模型在评估抑郁症症状方面的可靠性。研究人员发现,语言模型的选择和提示策略显著影响了抑郁症评分,占总症状评分差异的30%。即使在准确率很高的模型中,两名随机选择的评分者在约40%的参与者身上也存在筛查决策分歧。虽然校准提高了准确性并减少了分歧,但仍有相当一部分个体被不同评分者评估得不同。 AI

影响 强调了在LLM驱动的诊断工具中进行稳健校准和标准化的必要性,以确保患者评估的一致性和可靠性。

排序理由 该集群包含一篇发表在arXiv上的研究论文,详细介绍了语言模型能力的发现。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

语言模型在抑郁症评估中显示出高度的评分者依赖性

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该集群包含一篇发表在arXiv上的研究论文,详细介绍了语言模型能力的发现。[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) · Baihan Lin ·

    语言模型对抑郁症的评级更多地反映了评估者而非患者

    arXiv:2610.08501v1 Announce Type: cross Abstract: Depression has no diagnostic blood test. Language models promise tireless, consistent assessment, but can accurate raters disagree about individuals? We pre-registered 880 language-model raters, crossing 11 open models with prompt…