alt Hacker NewsWhy is AI generated language so hard to understand?
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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.
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.
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.
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.
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.
Its because AI was not optimized for human reading, its optimized for the most amount of information in the lowest amount of context or tokens as possible.
(e.g., "baseline system state transition latency validation") instead of unpacking them with prepositions ("validating the latency of state transitions in the baseline system").
For me, I can't read books at all, because I forget what was said two paragraphs before. I bet people that are good at skimming text have less problems with AI generated text.
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.
Especially for general purpose technical writing, system design, architecture and code comments, I do use pstack unslop [0] and technical-writing [1] skills. They do wonders.
[0] https://github.com/cursor/plugins/blob/main/pstack/skills/un... [1] https://github.com/cursor/plugins/blob/main/pstack/skills/te...
The think I don't understand is: why did the models become so much worse with time?
I seems to remember a distinct time during which Claude for example became incomprehensible and weird with its writing, whereas before it was reasonably OK, even if still not that good
AI has a way of using abstract language, sounding impressive and eloquent, but in reality it is mostly incoherent fluff. You can see this in humans too. Some politicians and academics are good at expressing themselves in clever ways but when you actually try to parse what they say it makes no sense what-so-ever.
It's like reading legalese.
Or reading patents.
Terrible ... like someone put in effort to make it unreadable.
It argues that training for "math, code, and reasoning" caused this. Do LLMs only trained for natural language (probably older models) have the same characteristics? Does training for these additional domains help in some ways, and this specific style of writing is just a side-effect of that?
This was great to read even if just to name the stuff that I find so irritating!
> AI-generated text is increasingly optimized for other goals like math, code, and reasoning
Yes, this is a problem.
AI-generated text is used internally by the AI to instruct agents, etc. And for feedback loops in reasoning. If you optimize for the result only, the AI will increasingly speak "AI", not human.
This is an absolutely critical issue; it's impossible to work with AI that can't communicate coherently. It's barely usable for reasoning on complex issues because of the language problems described exactly in that synopsis.
There's so much fluff because it doesn't care about what the user actually cares about.
So there's the usual structure of "it's not x it's actually y" signs.
I'm just glad it's not just me being stupid. Sometimes I have to read a sentence several times and then ask to elaborate because I can't comprehend.
Maybe I'm the odd one out, but I really don't find AI generated language, in almost any model, to be hard to understand. I'm not trying to be snarky or annoying or pompous or whatever word might be "this guy over here think's he special."
It uses a lot of metaphors. It references stuff implicitly - a LOT - and assumes pretty much all the time that the thing it did is what you saw, which you didn't. But if you just.. read the stuff it says, and know a few old-school sayings and thesaurusize your reading style, it usually says things quite meaningfully.
I think a lot of people just don't read what it writes. So many folks I've watched used LLMs don't write in complete sentences, don't read the sentences that come back, don't watch the turns and tool calls in the middle, and then just respond to the message as if it says something it doesn't. I'm saying all this before I read the paper, but I've done a lot of personal and team experiments with this, so I'm quite curious to see what they say. I'll edit this after I go through it.
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Fascinating! I did actually run into a lot of this with my personal 'engineering and communication styles' experiments. Most of the papers out there talk about models moving toward human language inside of a context window, and not the other way around (much smaller effect for human->model). I had a few years of conversations I ran on local and 'frontier' Claude and found that, yes, in general, information density is what each response turned out to be geared toward, whether it was everyday health discussions or deeper engineering topics and research. The component tests they ran I've been exposed to already, but I think their conclusion nailed it: ```This discrepancy may help explain why many frontier LLMs use linguistic constructions which are technically allowed by the grammar of English, yet are out-of- distribution for human language and are associated with comprehension difficulty.```
Indeed. I just struggle to revise it when generating website/social media post copies, more so now than before from what I can recall.
This was excellent, and one would hope they can at least try to optimize these metrics a bit.
My experience is: The LLM fails to "understand" or "intuit" who the audience is. It tends to reward its own recent context and its interaction with the prompt writer, rather than "considering" documents from the viewpoint of a hypothetical reader. You can often prompt them to go more in that direction but it gets tiresome.
The best approach is just to write the outline and initial text in a very messy fashion yourself. And then have it clean it up and add detail. And then edit again by hand.
I have seen people saying this for a long while now. I don't know how to say this but I understand Ai stuff better than non-Ai. When I see people complain ; I always wonder why because I think that its pretty good with my own senses.. Much much better than my writing skills... My brain has been tuned by computers I suspect .
I know the paper specifically calls out Opus 5.5 and Opus 5, but I can’t see different versions of Sol, or Astra.
I think the difficulty in comprehension was mostly an Anthropic problem… partly because Anthropic employees stopped reading Opus output around the time Fable came out and stopped noticing RLVR going off the rails with compression.
The paper was a good read, in any case. Maybe I gotta start sending this article back to LLMs when they start compressing text too much :)
I find AI generated language hard to understand when it is "in it's own world" and it knows what it means but can't articulate it without buzzwords and slop. A follow up "ELI5" type of prompt often sorts this out. Seems like a failure mode and you need to snap it out sometimes. Opus 5.5.
Article seems to answer the question: because generated text has the competing objective of information density and supporting reasoning chains.
But I also wonder why AI loves to add slogans, catch phrases, and witty sentence fragments. It’s grating and a dead giveaway of slop.
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Nah I don't have any issue understanding, so the actual question here is why do some people find AI-generated language hard to understand?
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.