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English(EN) How Order-Sensitive Are LLMs? OrderProbe for Deterministic Structural Reconstruction

新基准揭示 LLM 在精确文本结构重建方面存在困难

研究人员开发了 OrderProbe,这是一个旨在评估大型语言模型 (LLM) 重建文本精确结构顺序能力的新基准。与允许多种正确重排的先前方法不同,OrderProbe 使用固定的四字符中文、日文和韩文字符串来实现精确匹配评分。对十二个 LLM 的实验显示,即使是先进的模型也难以胜任这项任务,在零样本恢复中的准确率通常低于 35%,这表明语义理解与精确结构重建之间存在差距。 AI

影响 突出了当前 LLM 的一个关键局限性,表明需要改进专注于结构完整性的架构设计。

排序理由 该集群包含一篇介绍 LLM 评估新基准的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

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

新基准揭示 LLM 在精确文本结构重建方面存在困难

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该集群包含一篇介绍 LLM 评估新基准的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CL TIER_1 English(EN) · Zhaolu Kang, Yingjie He, Kehan Jiang, Leqi Zheng, Jiachen Qian, Qianyuan Zhang, Chunlei Meng, Yujie Feng, Yuan Wang, Stephen Dou, Aming Wu, Pengxiang Zhao, Jiaxin Liu, Guansu Wang, Zeyu Zhang, Lei Wang, Qishi Zhan, Xiaomin He, Meisheng Zhang, Jianyuan Ni… ·

    大型语言模型对顺序有多敏感?OrderProbe用于确定性结构重建

    arXiv:2601.08626v4 Announce Type: replace Abstract: Large language models (LLMs) excel at semantic understanding, yet their ability to reconstruct internal structure from scrambled inputs remains underexplored. Sentence-level restoration is difficult to evaluate automatically bec…