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English(EN) Molecular D\'ej\`a Vu: Digit-Level Retrieval of Published Values in Frontier Language Models

研究发现:前沿大型语言模型广泛存在分子数据逐字检索现象

一篇新发表在arXiv上的研究论文,调查了前沿语言模型中“分子似曾相识”的现象,即模型似乎是逐字检索已发布的分子属性值,而非进行预测。研究发现,这种逐字检索现象普遍存在,但在不同的回归基准测试中差异显著。有趣的是,当模型被赋予更高的推理提示时,检索率会显著增加。该研究还探讨了中断这种检索的方法,并提出模型的通用预测能力并非完全由记忆的值决定。 AI

影响 这项研究突显了大型语言模型评估中存在的潜在局限性,表明当前的基准测试可能由于记忆而无法完全捕捉真实的预测能力。

排序理由 该集群包含一篇详细介绍语言模型研究发现的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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研究发现:前沿大型语言模型广泛存在分子数据逐字检索现象

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该集群包含一篇详细介绍语言模型研究发现的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Matthias Busch, Marius Tacke, Sviatlana V. Lamaka, Mikhail L. Zheludkevich, Christian J. Cyron, Roland C. Aydin, Christian Feiler ·

    分子“既视感”:前沿语言模型中的数字级检索已发表值

    arXiv:2609.05381v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly evaluated on molecular property benchmarks, but accuracy cannot distinguish a model that predicts a property from one that retrieves a published number. We audit 22 frontier models on 12…