arXiv cs.CL· Thomas Hikaru Clark, Matias B. Martin, Kyle Mahowald·· 13 小时前AI 评分48
研究揭示 AI 生成文本为何越来越难懂：九大前沿模型的语言特征分析
Why is AI-generated language so hard to understand?
AI 导读
一项针对 AI 生成文本的心理语言学研究显示，九个前沿及准前沿模型比人类写作更频繁使用无生命主语搭配有生命动词、低频 n-gram，多数模型还使用更长依存距离、更多名词堆叠和缩减宾语关系从句，且不同模型家族间差异显著。研究提出，人类语言为成功沟通而优化，AI 文本则日益为数学、代码和推理等目标优化，追求信息密度而非可理解性。通过在长度惩罚下微调语言模型，研究者为该假设提供了概念验证。
正文 · 原文
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.
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.29162 [cs.CL] |
| (or arXiv:2610.29162v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2610.29162 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Thomas Clark [view email]
[v1]
Wed, 21 Oct 2026 03:54:08 UTC (528 KB)
来源：arXiv cs.CL · arxiv.org