AI-Generated Language Difficulty Causes

The paper identifies specific linguistic features, such as low-frequency n-grams, long syntactic dependencies, and rare verb forms, that AI models consistently overproduce, leading to difficult-to-understand text. A controlled reinforcement-learning intervention shows that enhancing task efficiency penalizing length leads to more compressed, harder-to-read sentences. The findings suggest that AI model language deviates from human-like preferences due to post-training objectives emphasizing information density over comprehension. LLM Chatbots are increasingly trained for coding, mathematical reasoning, and tool use, where semantic correctness is rewarded without concern for language being easy to understand. Research question and central thesis The paper examines why contemporary LLM outputs are often perceived as unusually difficult to read despite being grammatically well formed. Its central empirical claim is that frontier and near-frontier models systematically overproduce linguistic configurations associated with human processing difficulty. The analysis focuses on rare or low-predictability n-grams, long syntactic dependencies, stacked noun compounds, denominal verbs, inanimate subjects paired with animate-selecting verbs, and reduced object relative clauses. The authors formulate two hypotheses. H1 predicts that frontier and near-frontier models will overuse features associated with comprehension difficulty. H2 predicts that post-training objectives emphasizing information density, task success, or token efficiency will shift model language away from human-like preferences. The paper supports both hypotheses through a large comparative corpus analysis and a controlled reinforcement-learning intervention (2610.29162). The theoretical explanation is grounded in a contrast between human communicative production and model optimization. Human language production is constrained by incremental processing, lossy working memory, and the need for successful communication with other humans. By contrast, LLMs are increasingly post-trained for coding, mathematical reasoning, tool use, and verifiable task completion. These objectives can reward semantic correctness without rewarding linguistic forms that minimize comprehension effort. The paper therefore treats difficult model language not simply as a stylistic artifact, but as a possible consequence of objective mismatch. Corpus design and analytical framework The first experiment evaluates outputs from 14 …