计算机科学 > 计算和语言
[于2026年10月21日提交]
标题：AI生成的语言为何如此难以理解？.
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摘要：普遍感觉AI生成的文本最近变得难以理解。 在这项研究中，我们调查了一套已知与人类加工困难相关的心理语言特征(如对象相对条款，长的合成依赖性，低频n克). 我们发现，AI模型使用这些困难的特征比圆通翻译生成的译文要多。 与人类的写作相比，所有9个前沿和近边模型都使用了更多有动词和更低频正方克的无生命主体，大多数还使用了更长的依赖性，更多的堆叠的名词，以及对话中更多的减少对象亲属；我们还展示了模型家族之间的巨大差异。 我们提出并测试一个假设，说明为什么：虽然人类语言被优化用于成功的交流，但是AI生成的文本被越来越多地优化用于数学，代码和推理等其他目标，其中存在\textit{信息密度}而不是成功交流的压力。 我们用微调来证明这个假设 del在长度惩罚下，这导致了与人类类似语言行为的差异。 我们的研究结果突出了人类语言处理要求与LLMs之间错位的轴线，并指出了可能的补救办法。
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Computer Science > Computation and Language
[Submitted on 21 Oct 2026]
Title:Why is AI-generated language so hard to understand?
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Abstract:There is a widespread sense that AI-generated text has recently become harder to understand. In this study, we investigate a set of psycholinguistic features that are known to correlate with human processing difficulty (e.g., object relative clauses, long syntactic dependencies, low-frequency n-grams). Using a novel corpus of AI-generated text across diverse domains, we find that AI models use these difficult features more often than paraphrases generated by round-trip translation. Compared with human writing, all nine frontier and near-frontier models use more inanimate subjects with animate verbs and more low-frequency n-grams, and most also use longer dependencies, more stacked nouns, and more reduced object relatives in conversation; we also demonstrate substantial variation across model families. We propose and test a hypothesis as to why: while human language is optimized for successful communication, AI-generated text is increasingly optimized for other goals like math, code, and reasoning, in which there is a pressure for \textit{information density} rather than successful communication. We provide a proof-of-concept for this hypothesis by fine-tuning a language model under a length penalty, which leads to a divergence from human-like linguistic behavior. Our findings highlight the axes of misalignment between linguistic processing demands in humans and LLMs, and point toward possible remedies.
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