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English(EN) Pretraining Data Can Be Poisoned through Computational Propaganda

研究揭示大型语言模型预训练数据易受网络内容投毒攻击

作者 PulseAugur 编辑部 · [2 个来源] ·

一篇新研究论文探讨了大型语言模型（LLM）预训练数据易受投毒攻击的脆弱性。该研究表明，恶意内容可以通过公共讨论界面注入，这种方法难以检测和缓解。为解决此问题，研究人员开发了一种名为HalfLife的新型分析工具，用于估算网络爬取训练数据中对抗性内容的包含情况，并指出第三方网页内容是一个潜在的攻击向量。 AI

影响 凸显了大型语言模型训练数据中一个关键的安全漏洞，可能影响模型的安全性和可靠性。

排序理由 该集群包含一篇详细介绍大型语言模型训练数据分析新方法的 ist.

在 arXiv cs.AI 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

研究揭示大型语言模型预训练数据易受网络内容投毒攻击

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该集群包含一篇详细介绍大型语言模型训练数据分析新方法的 ist.
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报道来源 [2]

  1. arXiv cs.AI TIER_1 English(EN) · Victoria Graf, Hannaneh Hajishirzi, Noah A. Smith, David Kohlbrenner, Kyle Lo ·

    预训练数据可通过计算宣传进行投毒

    arXiv:2607.15267v1 Announce Type: new Abstract: Poisoning pretraining data can introduce harmful behaviors to LMs that are difficult to detect and mitigate. Prior work on poisoning pretraining data has largely exploited established data sources such as Wikipedia, which do not rep…

  2. arXiv cs.AI TIER_1 English(EN) · Kyle Lo ·

    预训练数据可通过计算宣传进行投毒

    Poisoning pretraining data can introduce harmful behaviors to LMs that are difficult to detect and mitigate. Prior work on poisoning pretraining data has largely exploited established data sources such as Wikipedia, which do not represent the large scale and heterogeneity typical…

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