Hacker News Front Page · [Submitted on 21 Oct 2026] ·
Why is AI-generated language so hard to understand?
El estudio encuentra que nueve modelos de IA de frontera y cercanos a ella usan con más frecuencia que las personas varias estructuras lingüísticas asociadas con una mayor dificultad de comprensión, aunque el patrón varía entre familias de modelos. Propone que la optimización para tareas como matemáticas, programación y razonamiento favorece la densidad de información por encima de una comunicación fácil de procesar; un experimento con una penalización por longitud aporta evidencia inicial a esta explicación.
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.