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English(EN) Not Self-Decidable: LLMs Cannot Draw the Boundary of What an Agent Verifier Can Check

新论文发现LLM无法自我确定规则边界

一篇新的arXiv论文认为,大型语言模型(LLM)无法确定它们可以执行的规则范围,这一概念被称为“自我判定”。研究人员发现,LLM在解释复杂的监管文本时一致性较差,并且在评估自己的规则集时倾向于朝着同一方向出错。该论文引入了一种名为CoVer的新验证方法,该方法使用一致性和基于干预的检查来为代理验证建立更可靠的边界。 AI

影响 强调了LLM在规则执行方面的局限性,并提出了一种新的验证方法,影响了可信赖AI代理的开发。

排序理由 该集群包含一篇发表在arXiv上的学术论文,详细介绍了新的研究发现。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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新论文发现LLM无法自我确定规则边界

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该集群包含一篇发表在arXiv上的学术论文,详细介绍了新的研究发现。[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) · Anthony Rhodes ·

    不可自行判定:大型语言模型无法划定代理验证器可检查的边界

    arXiv:2610.04699v2 Announce Type: replace Abstract: A verifier for an agent faces rules of two kinds: the ones a fixed check can settle and the ones that require a judge. A team that derives its own checks fixes that split up front. Where the requirements come from outside, as in…