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English(EN) Auditing Chinese Web-scale Corpora via Sampled BPE Token Statistics

新方法审计中文网络语料库以检测大模型污染

研究人员开发了一种名为 Sampled-BPE 的新方法,用于高效审计大型中文网络语料库中的大模型污染。与完整扫描相比,该技术显著降低了运行时间和内存使用量,同时保持了对污染类别的准确估计。该流程应用于多个公开的中文语料库和 Common Crawl 快照,揭示了普遍存在且随时间变化的污染。此外,还发布了一个包含上下文和类别信息的中文网络标记的详细数据集,以协助进一步分析和追踪污染。 AI

影响 为清理和验证用于训练大型语言模型的数据提供了一种更有效的方法。

排序理由 学术论文,详细介绍了一种新的语料库审计方法。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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

新方法审计中文网络语料库以检测大模型污染

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学术论文,详细介绍了一种新的语料库审计方法。[lever_c_demoted from research: ic=1 ai=1.0]
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完整方法见我们的编辑标准。

报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Qingjie Zhang, Ziqi Tang, Jie Zhang, Gelei Deng, Jinfeng Li, YueFeng Chen, Yitong Yang, Hui Xue, Tianwei Zhang, Han Qiu ·

    通过采样 BPE Token 统计审计中文网络规模语料库

    arXiv:2608.10678v1 Announce Type: cross Abstract: Chinese web pollution has surfaced in LLMs, motivating audits of upstream Chinese corpora. However, auditing such corpora faces three challenges: (1) their web-scale size makes full scan costly; (2) prior analyses are often too co…