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English(EN) The strength of clinical evidence is recoverable from language model representations but not from their stated grades

大型语言模型（LLM）内部保留临床证据强度信号，但无法表达出来

作者 PulseAugur 编辑部 · [2 个来源] ·

一项新研究表明，大型语言模型（LLM）能够在其内部表征临床证据的强度，即使它们无法在其声明的评分中表达这种信心。研究人员发现，一个线性估计器可以从LLM表征中恢复这种证据强度信号，其中位数 AUROC 为 71.8，尽管该信号在很大程度上是词汇性的，并且没有随着模型规模的增大而提高。尽管存在这种内部信号，但模型对证据强度的声明评分常常处于随机水平，这表明其内部理解与其外部沟通之间存在脱节。 AI

影响 突显了LLM沟通中的一个差距，表明在临床应用中有可能改进置信度评分。

排序理由 该集群包含一篇详细介绍LLM能力研究结果的学术论文。

在 arXiv cs.IR (Information Retrieval) 阅读 →

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大型语言模型（LLM）内部保留临床证据强度信号，但无法表达出来

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该集群包含一篇详细介绍LLM能力研究结果的学术论文。
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报道来源 [2]

  1. arXiv cs.AI TIER_1 English(EN) · Soroosh Tayebi Arasteh ·

    临床证据的强度可从语言模型表征中恢复，但无法从其声明的等级中恢复

    arXiv:2606.29034v1 Announce Type: cross Abstract: Large language models (LLMs) increasingly summarize clinical evidence, where a claim's weight depends on how strongly it is supported. Yet these models convey confidence poorly, and properties they never state, such as truth, are …

  2. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Soroosh Tayebi Arasteh ·

    临床证据的强度可从语言模型表征中恢复，但无法从其声明的评分中恢复

    Large language models (LLMs) increasingly summarize clinical evidence, where a claim's weight depends on how strongly it is supported. Yet these models convey confidence poorly, and properties they never state, such as truth, are often readable from their activations. Whether a c…

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