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English(EN) The Dynamics of Human and AI-Generated Language: How Semantics Fluctuates across Different Timescales

新方法分析人类和人工智能语言中的语义时间尺度

研究人员开发了一种新方法来分析人类和人工智能生成语言中语义内容的 temporal dynamics。该 pipeline 使用 WordNet 深度和 SBERT 嵌入来创建语义时间序列,然后使用自相关窗口度量进行分析。研究发现,语义时间序列中较长的自相关窗口与更通用的词汇相关,而较短的窗口与特定单词相关,这表明语言中存在非平凡的 temporal organization。 AI

影响 提供了一种新的定量方法来比较人工智能生成语言与人类语音的时间结构。

排序理由 该集群包含一篇学术论文,详细介绍了一种分析语言的新方法。[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 →

新方法分析人类和人工智能语言中的语义时间尺度

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该集群包含一篇学术论文,详细介绍了一种分析语言的新方法。[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) · Han-Jen Chang, Yasir \c{C}atal, Angelika Wolman, Agust\'in Ib\'a\~nez, David Smith, I-Wen Su, Kai-Yuan Cheng, Georg Northoff ·

    人类与AI生成语言的动态:语义在不同时间尺度上的波动方式

    arXiv:2606.11371v1 Announce Type: cross Abstract: Spoken language, whether produced by humans or large language models (LLM), unfolds over time with varying semantic content. However, we still lack simple, interpretable time-series features that capture how generic versus specifi…