Why is AI-generated language so hard to understand?
- 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.
Tasks
- Pinged Carlos, Antonio, and Mateusz about giving a talk
- Still need to ping Jimmy, Shemei (and Alden?). Done
- Ping Jeff. Done. Interesting and mysterious pingback!
- Add something about ECHOTRIBBLE to the blog post. Done
SBIRs
- Slides for sprint review – done
- Stories – done
- Sprint planning – done
- Added text_tab_data property so that pasted text can be seen by the model. It’s a little clunky in that you have to click the “submit” button to load it into the variable. Maybe I should just change the name of the button?





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