AI text is hard to read because models optimize for information density, not communication
Why is AI generated language so hard to understand?
A new arXiv study measures psycholinguistic features tied to human processing difficulty across a corpus of AI-generated text. All nine frontier and near-frontier models use more inanimate subjects with animate verbs and more low-frequency n-grams than human writers, and most also produce longer syntactic dependencies and stacked nouns in conversation. The authors argue that human language is optimized for successful communication, while AI text is increasingly optimized for math, code, and reasoning, where information density matters more. A length-penalty fine-tuning experiment offers proof of concept.
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 information density rather than successful communication.
- dividefuel
As a technical writer, I believe default AI responses are optimized for what people _think_ is good writing rather than what actually is good writing.
My guess is years of RLHF optimized for rater's first impressions of how well-written something was. It uses lists! It has headings! It uses fun phrases like 'smoking gun'! It's long and informative! These things are relatively fast and easy to identify and select as a preference.
But none of this really covers whether the response is _useful for the audience_. If you're not the audience, it can be hard to tell what is and isn't useful.
- cryzinger
One construction I'm surprised the paper doesn't mention (although, to be fair, I'm sure they had plenty of options to choose from... and you have to draw the line somewhere :P) is that LLMs love to use transitive verbs intransitively[0], in a way I almost never see in human writing. At best it makes sentences weirdly ambiguous, at worst it makes them borderline incomprehensible, and it's especially annoying in technical contexts where precise language should be non-negotiable. Like, yes, I can make an educated guess about what it's trying to say, but if your so-called documentation requires readers/users to make educated guesses then something has gone terribly wrong.
[0] An example would be something like:
"The user presses a button, and the form submits."
There are valid intransitive uses of "submit," but none that make sense in this context. In this case "submit" either requires an object (like "the form submits data"--plus maybe an indirect object, like "the form submits data to the server") or should be swapped for "the form is submitted" if you absolutely don't want to supply an object.
- armchairhacker
Anecdotally, AI is very bad at distinguishing the important and relevant from the mundane. I tell AI to suggest changes to the “high-level specification” and it constantly suggests documenting arcane edge-cases and implementation details.
- lukev
LLMs are showing us that a lot of what we think of as cognition is actually part of the elaborate symbol system which is Language, as opposed to a property of "mind" (either biologically or artificially instantiated.)
LLM-generated text is hard to understand because while it's perfectly good language that follows valid cognitive and informational patterns, it does not reflect the intent or mental model of another mind.
- orbital-decay
As usual most commenters reply to headlines, so I quote their conclusions.
>We take these results as a proof of concept that a length penalty used during reinforcement learning with GRPO can directly affect grammatical features. We note that the model was not directly instructed to use compressed or dense language, and was not directly given information about the availability of syntactic alternations. Rather, the penalty resulted in an emergent preference or shorter members of syntactic alternations. [...] We speculate that the use of verifiable rewards creates an environment where correctness is decoupled from humanlike communication, leading models to converge toward a novel, potentially less comprehensible distribution of language use.
This sounds like common sense to anyone who tinkered with the models and training, however someone still needs to quantify it so it's good they did. I also want to note that this is only visible in Claude's default register, it's still able to write differently when prompted.
- anigbrowl
Consider too that AI training corpuses include vast amounts of commercial speech which is designed to numb the critical faculties and produce action; likewise they include numerous opinion articles, which are designed to persuade and often leverage rhetorical fallacies, whether cynically or as a reflection of an author's imperfect thought process.
- noosphr
AI generated text has no human centered underlying model of physical reality and its metaphors are tortured as a result.
If we had talking centipedes the results would probably be similar, but for different reasons.
- lxe
https://gist.github.com/lxe/4cd2ca0fd3b714b25f138227c8c650d3
I've been iterating on this writing clarity prompt. The term we came up with was "Asyndetic parataxis with referential ellipsis and repeated rhetorical closure." It doesn't fully describe the exact issue I have with modern AI writing, but it's close.