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.<p>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.<p>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.
It really hit home for me when Google shipped their 'writing help' feature or whatever in gmail. Pretty much every sentence I wrote, it had useful tips for me like "make this more concise" and "reduce run-on sentence". No joke, I averaged about 1.3 suggestions per sentence.<p>And pretty much universally, they were awful. It was like somebody had blindly taken an English writing textbook and applied every little helper rule to every clause of every sentence, paying no attention whatsoever to deeper concepts like pacing, drawing attention to key words, and moderating emotion.<p>And I think that's basically what's going on in all of the frontier models as well. When they are being fine tuned, some reward score is telling them to be concise, not to use filler words, to avoid run-on sentences, etc. And there's no counter-balance in the fine-tuning that looks at the writing and flow as a whole.
[For example, gmail suggests]<p>"Google shipped their" -> "Google shipped its" (I use 'their' to make it clear I'm talking about the people who build features at Google, and not merely about a faceless corporation)<p>"feature or whatever" -> "feature" ('or whatever' is chosen to tell everyone that I'm not actually sure what the name of the feature is, and I can't really be bothered to figure it out)<p>"gmail" -> "Gmail" (meh)<p>"I wrote, it had useful tips" -> "I wrote received useful tips" (if you remove the comma, the pacing changes, the reader doesn't linger on the word 'wrote', and you invite some ambiguity around the writing and the receiving of useful tips. I feel like this suggestion is particularly brain damaged, I simply can't understand my own writing when I make this change)<p>(and so on. I now write emails in notepad, and copy-paste them into Gmail when I'm done, that's how much the suggestions annoy me).
Pacing is extremely important in writing. The way one speaks out loud is often not the way that they actually read in their head. Speaking out loud has three parts to it in conception, translation, and vocation. Conception as to what the thought is, translation of that thought to language, and vocation of the language through visuals or sound. Reading only shares the translation portion, but also has organization added to it where you need to organize the information as you ingest it. As such, it's important to format whatever you're writing so that it can be organized first and translated second. People look at the words linearly but don't ingest the information linearly. This is a principle many literary jokes are built upon.<p>ChatGPT, Gemini, and whatever else do not in fact have the capability to understand that interaction between organization and translation. They process the information linearly, organize it linearly, and translate it linearly. That's what makes it so strange to read. A must lead to B which must lead to C. A cannot connect to C and then circle back to B which is connected to both A and C obliquely. It's a failure in understanding language and how it is used.
I learned in school (English is my 2nd language) that you can never use "their" or "they" about something inanimate like a corporation (or a chair, for example). You would use "its" or have to rephrase your sentence; "the developers at Google shipped their feature."<p>The suggestions are valid and correct, we (generic "we") are just used to not following proper grammar. This is how languages evolve but it is also what puts these assistance tools in a peculiar spot, because they help with accuracy, not everyday expressions.
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> I learned in school (English is my 2nd language) that you can never use "their" or "they" about something inanimate like a corporation (or a chair, for example).<p>Definitely not a chair, but a corporation is also "just" a group of people. "Their" makes perfect sense for a corp, even if it isn't perfectly accurate
LOL. The trick with Ai is to write whatever you want in any which way you like and it understands it no matter how shitty it is. Good writers should avoid asking Ai for suggestions. Bad ones like me, don't bother asking anyways. Too lazy to fix it all.
As someone who has been on the other side and has read a lot of vendor technical writing, this is my default instruction, either to be stored long term or in that session: "I expect concise and clear replies, in ASD-STE100 compliant text."<p>So far at least this trims down the verbosity and non-sense idioms in all the models I've used.
Yea the hypothesis seems the exact opposite of what I find with LLM writing. It’s not that it’s overly dense instead I find that it’s hard to read because it’s so information sparse. Like you’re saying it feels optimized to “good writing” not to conveying information.
> 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.<p>At least in the case of ChatGPT - it was (and mostly still is) objectively optimized for engagement. Since ChatGPT was leading the way, and much distilling has occurred, there are at least partial remnants of that problem everywhere.<p>The Chinese models may or may not really care about this at all, but they're doing a lot of distilling, so they're getting it whether they want it or not.<p>The American models for the most part care about engagement - some a lot more than others.
I'm a writer, a technical writer, and a highly technical person in the executive space. Communication has always been one of my strong points, and the blathering on that AI does drives me to distraction. It's impossible to really tailor the output of LLMs very much. I value information dense, direct writing when it comes to technical things and they're always embellishing and going off on side quests and it's obnoxious.
I feel like also all the code optimizations make it treat all language like code and that produces patterns that align with how it generates code. Earlier models that focused on language seem to have done better - 4o seems like language wise it’s pretty good but advancements elsewhere make it difficult to use.
As both a writer and SWE the problem with LLM output is the same. They can achieve localized goals but have trouble juggling multiple concerns at the same time.<p>Things that are easy for humans, like remembering a character is holding a gun during a conversation, gets forgotten, because there is no clear architectural/spacial understanding of how the words are describing an underlying reality. And if the LLM gets this right it might instead forget which character has a Southern drawl, or make an illogical metaphor.<p>This is all more clear with programming, but the problem is the same - a lack of interwoven semantic understanding and friction between the real world and the self-contained world of the LLM's context.
This. The only “verifiable” writing is relatively short and verified through comprehension of that text. The way it writes is successful for that, but it doesn’t extrapolate to longer form, and under frequent use becomes problematic (sloppy).
I actually think they stopped caring about the prose a long time ago. Now all the resources get poured into making sure the right tool is called, remember the labs dont want humans to be the main users of these in the end. They want them to autonomously work on their own or with each other, and another agent doesn't care how an agent writes.<p>Maybe we'll get assistant models that will get optimized for human readable prose, but I think the labs have deluded themselves when it comes to utility/coding models and think the humam isn't going to be a variable soon.<p>I have a jira-esque ticketing system for agents/subagents and the tickets are basically unreadable to me. I hate it. But if I force them to write the tickets in a prose I prefer their preformance degrades. Its almost like this gibberish hard to read prose is designed for llms to communicate betweem themselves probably because the llms are doing most of the RLHF (RLAIF??) for training now.
As usual most commenters reply to headlines, so I quote their conclusions.<p><i>>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.</i><p>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 the default register of the model, it's still able to write differently when prompted.
<a href="https://gist.github.com/lxe/4cd2ca0fd3b714b25f138227c8c650d3" rel="nofollow">https://gist.github.com/lxe/4cd2ca0fd3b714b25f138227c8c650d3</a><p>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.
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.)<p>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 <i>mind</i>.
> 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<p>This is entirely speculative and highly disputed, even if you are doing the more sophisticated thing here and by Language you mean something like mentalese.<p>Much cognition is embodied and makes use of visual, tactile, auditory, kinaesthetic, and other sense modality representations. "Thought is mostly a Language" has been a wordcel kind of argument with little empirical or philosophical support for a couple of decades now, IMO. Language is how we <i>communicate</i> thought, not primarily how we think.<p>Not that this refutes your main point: LLMs are trained <i>almost entirely</i> on (written) language, and since language is only a single, limited aspect of real cognition, this makes them so annoying and alien to read.
I think we're in violent agreement here. Language is <i>part</i> of how we think. Definitely not the whole thing -- we are physically and biologically instantiated and LLMs are not. Language is a software, but the hardware does matter.<p>But LLMs DO show us that language is a big deal. It isn't just a serialization protocol for real thought -- it's a material component of <i>how</i> we think.<p>In many ways I think LLMs are the strongest possible proof of structuralist and post-structuralist theory which argues that human cognition is conditioned on the language and concepts to which we have access.
Consider too that AI training corpuses include vast amounts of commercial speech which is designed to numb the critical faculties and produce action; likewise it includes 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.
Exactly. It’s been trained on petabytes of clickbait, engagement-farming content.
The slop-in, slop-out theory. I'm very much an advocate.<p>The neat thing about LLMs is that in a way they proved that only the truth turns out to be grammatical (statistically), largely because there are so many more ways to be wrong than right that the mode is almost always right.<p>But people also lie and bullshit in consistent ways, with the intention of fooling people with well-known empty arguments. Part of being a "good writer" is to make the meaningless sound meaningful - and the techniques are ancient.<p>Train on a bunch of people trying to push their framework and get rich, whether in online garbage, literary garbage, or in ostensibly serious nonfiction or scientific works, and you'll end up with modal language that is trying to play deceptive games with reasoning, but has nothing in particular it's trying to convince you of (no motives.)<p>People would either fall for that language (if they weren't thinking much and the context was one that comforted them), or they would methodically try to put that language into a model (let's say a series of syllogisms for an argument about what to do or what to believe, but it could be any model), and see that it failed. And speaking again of the context, the context of humans interacting with human content implies motives that help the listener focus on particular parts of their model to look for rhetorical tricks. The LLM, again having no motive, just does this in random places.<p>The latest advances in AI have been in the tooling around AI, which I think largely reduces to recursively creating models and populating them in outputs short enough that hopefully the text generation won't go off the rails.<p>Symbolic AI is coming back, it's just that the mortar is random word association.<p>Call me crazy, but I think Georges Bataille saw all of this. In my interpretation, he simultaneously thought of random associations as the most important component of reason and also completely irrelevant in the reality of the world. The only way you could discover the structure of things is through random collisions; your view of the universe was entirely constructed by your prejudices, lusts, and fears; but whether that view was wrong or right was based on how it <i>worked</i> and the particular content of the id-garbage that got you there was ultimately irrelevant. The fabric of the world is completely id-less, but can only be digested by id-driven beings. LLMs (without the tooling around them) are all id.
AI generated text has no human centered underlying model of physical reality and its metaphors are tortured as a result.<p>If we had talking centipedes the results would probably be similar, but for different reasons.
Also the training punishes clearly wrong text harshly compared to not clearly wrong text.<p>Which gives a relative incentive to hard to understand text.
That, and one can feel that there is no intention behind the text. There are no introductions and main points and fun asides, just a mechanical, steady stream of words with much too frequent, quasi-randomly distributed fake "main points".
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.<p>(e.g., "baseline system state transition latency validation") instead of unpacking them with prepositions ("validating the latency of state transitions in the baseline system").<p>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.
In my mother tongue "baseline system state transition latency validation" would have grammatically been one word. I sometimes miss that in English. If you're tired and see "baseline system state transition*s* latency validation", note the hallucinated "s", the meaning changes and you need to read it again because you're unsure if you actually <i>did see</i> an "s" or if it's "one thing".<p>"Baselinesystemstatetransitionlatencyvalidation" is surprisingly enough less confusing to me because I know it's one word describing <i>one thing</i>, and my eyes registers each joined word with (years of) training, even though what my eyes look at might be the following chunks in order:<p>1) [ Baseline ] systemstatetransitionlatencyvalidation<p>2) Baseline [ systemsta ] tetransitionlatencyvalidation<p>3) Baselinesystems [ tatetransitio ] nlatencyvalidation<p>4) Baselinesystemstatetransiti [ onlatency ] validation<p>5) Baselinesystemstatetransitionlaten [ cyvalidation ]<p>My eyes essentially scan the word, and although it might be a tad bid slower than reading "baseline system state transition latency validation", reading <i>and</i> understanding is faster because I already know it's <i>one thing</i> before I know which thing it is.<p>Edit: The example would be easier to demonstrate if HN supported bold font, but I don't think it does (or know how).
> its optimized for the most amount of information in the lowest amount of context or tokens as possible<p>Is it? I mean they get payed more if they return longer answers.
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.<p>Or reading patents.<p>Terrible ... like someone put in effort to make it unreadable.
Well that’s exactly the authors’ claim. Legalese and patents intent is not to facilitate communication but to maintain a claim under the most bad faith, hostile reading.<p>AI language, it seems, is optimized to support reasoning from the interior monologue.
I thought patents were <i>deliberately</i> bad? Like, the point is to get the protection, despite paying as little lip service as possible to the "it's in society's interest that inventions get published" side of it?
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.
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?
LLM reinforcement learning improves those tasks by having the agents "reason" until they hit a verifiable output state that passes a black or white test. So they learn language which is tuned to the task in a silo, never communicating with anyone or anything.<p>In contrast, most human communication and writing is intended for other humans, and we constantly hit failures here that we learn to iterate on and improve. Humans who do math do so with the constraints that their math needs to be reviewed by another human and in the context that all their writing needs to be understood by another human.<p>The process causes a warped and compressed version of language.
This was great to read even if just to name the stuff that I find so irritating!
There's so much fluff because it doesn't care about what the user actually cares about.<p>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."<p>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.<p>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.<p>--<p>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.```
> AI-generated text is increasingly optimized for other goals like math, code, and reasoning<p>Yes, this is a problem.<p>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.
But, if I understand this correctly, if we make it speak "human", then we lose the accuracy of the results, and we're back in hallucination land.<p>I mean, maybe that's all right for when it's producing code - code is not beautifully flowing prose in the first place.
I think we can make it do both, but it is much harder because we have no algorithm to validate an AI's internal mumblings against proper, human-understandable English.<p>Code is not prose, yes, but as others pointed out, it is certainly nice if code is maintainable by humans.
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.<p>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.
Yeah this drives me up a wall. You ask any AI to make a change and then it thinks polluting your README.md with those details is appropriate. No I don't think "this project deliberately does [random detail]" is needed when that detail is irrelevant outside of recent context.<p>I'm to the point where I don't trust it to do any writing. It's only allowed to tell me what it thinks I missed. Because inevitably when I ask it to edit my prose, it destroys the text and I basically need to rewrite it all again.
What do they mean by "low frequency n-grams"
A combination of words that are infrequently observed in natural language. For example in the preceding sentence some of the 3-grams are "combination of words", "infrequently observed in", and "words that are". N-grams are just this for all values of n. They are saying that often times the exact sequence of a short fragment of text is not very common in human written text.
Rarely used N length chain of words.<p>I.e. a high frequency 3-gram is "by the way".<p>A low frequency 3-gram is "elephants speak french".
But if you go too far the other direction, too <i>many</i> common n-grams, then it becomes platitude-speak, and nobody wants to read that either.
I don't think the paper really deals with that side of things. Just readability in terms of cognitive load.
correct, and that's largely why writing is a creative endeavour.
Unusual word combinations, basically. They’re word combinations that are rare in the training data, i.e. rare in normal language, but used more frequently by AI.<p>They’re difficult for humans because if a sequence of words hasn’t previously been seen, you have to expend more effort to make sense of it.<p>Something like “customer onboarding workflow automation" might qualify - it makes sense, but it’s not a commonly used phrase. AIs use a lot of these jargony sequences.
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 .
Have you read entirely generated documentation of code? I wouldn't say it's not understandable but it's no where near good quality human documentation.
I may have, don't know who is writing it, human or Ai.. All the same to me. If it has jargon though, I assume that it is humans.
I think the problem is that LLMs are verbose. They’re like business or self-help books— a lot of filler.
I skimmed the paper, and it seems to be saying something different.<p>Basically, there are often different ways of putting a particular idea into words. Sometimes the longer way might be clearer, and the shorter way might be more confusing. If you care about being understood, sometimes you will use the wordy option. This is what humans usually do.<p>However, LLMs have context windows. All else being equal, verbosity eats up more context. If the LLM is trained (with reinforcement learning) to reason most effectively, it can do that by using terse phrasing. Especially on certain tasks that really need a large context window. Why? Because being terse is like getting a larger context window for free.<p>But in another sense, it's not really free. You're trading off clarity for context window. Sometimes you have to choose between verbose+clear and terse+unclear. If you're biased toward terse, that will also shift you toward unclear.<p>I do agree that LLMs are verbose sometimes, though. I think they are also trained to please the user, and they see sharing extra knowledge and insights as a way to do that. I don't think this idea is in conflict with what the paper says. If an LLM's training rewards both spitting out neat bonus info and doing reasoning that requires making the most of its limited context window, then that LLM may generate a large quantity of text which is written in a slightly terse and confusing style.
I understand what you are saying but I personally can't tell what it is that people like or dislike other than myself. In a way I accept differences in grammars and assay formation in any which way that is produced but some scientific journals written by humans are unreadable in one pass without reading them over and over. Ai produces much simpler language . To me Ai is saying things in the way that my brain can process quite easily.
I think it’s a reading comprehension level kind of thing. If I slow right down, it’s all there and understandable. But the quality of prose is terrible. It doesn’t lend itself to fast skimming. So it feels like I have to slow way down to understand it. To be fair a lot of humans at work write this poorly as well.
I know the paper specifically calls out Opus 5.5 and Opus 5, but I can’t see different versions of Sol, or Astra.<p>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.<p>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.
Yes, I tend to find the longer a context window grows, the more the model tends to come up with its own jargon to describe previous findings, which, if you haven't kept up, starts sounding incredible dense; certainly not something I'd share with anyone else, i.e. in a PR comment.<p>I regularly prompt it now to "summarize again without the jargon". That helps, for now, to bring it back to using plain language. Together with my basic prompt to reply using ASD-STE100 Simplified Technical English (<a href="https://en.wikipedia.org/wiki/Simplified_Technical_English" rel="nofollow">https://en.wikipedia.org/wiki/Simplified_Technical_English</a>)
This is funny to me. For a while, I thought that Google's Gemini could give good insight and guide me better than a Tech Support representative could. It certainly seemed the right choice if I had questions about my Chromebook or Pixel Phone, or Android.<p>So I tried it out, and it really sometimes works, and I've overcome some real obstacles that Tech Support was unable to assist me with. However, Gemini can also become "too big for its britches" and begin explaining glitches from a systems-programming perspective, as if it has deep knowledge of the Android codebase, and deep engineer's knowledge of the Pixel system hardware. And it will spit out absolute nonsense.<p>I have retorted to it a few times, "that sounds more like <i>Star Trek</i> than a sound appraisal of the subsystem" and then Gemini smirks and says "You're right to call me out on that!" and blithely continues on its way.<p>So yeah. Sometimes the LLM confabulations are deeply cloaked in highly technical jargon that could not even be questioned, unless you already know it's absolute bullshit. And then the LLM freely admits it! LLMs have no shame, that's for damn sure.
Article seems to answer the question: because generated text has the competing objective of information density and supporting reasoning chains.<p>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.
Because people love to add slogans, catch phrases and witty sentence fragments, and AI was trained to mimic people. AI has shone a spotlight on it, and because AI isn’t perfect, it tends to overuse and use it where it shouldn’t. But go read news articles, blogs and corporate press releases from before the AI boom and take notice how often AI “tells” also show up there. The internet made hot take mic drop sentence fragments, “witty” turns of phrase and clickbait “it’s not foo, it’s bar” cliches a core part of its lexicon. It really shouldn’t be surprising that it shows up in AI too.
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Nah I don't have any issue understanding, so the actual question here is why do <i>some people</i> find AI-generated language hard to understand?
> 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?<p>As far as I can tell, it's only fluent readers who have trouble.
FWIF, I find I don't have much trouble. I think it's related to how I'm a software developer who also found as an adult learner German easier than French. From the abstract:<p><pre><code> inanimate subjects with animate verbs
</code></pre>
process waits for input, a method threw an exception, server accepts the request,<p><pre><code> more stacked nouns
</code></pre>
Ahem: `SimpleBeanFactoryAwareAspectInstanceFactory`<p><pre><code> long syntactic dependencies
</code></pre>
German has longer syntactic dependencies than English because readers (and listeners) often must *under* several intervening clauses of the sentences read before they can *stand* the split-apart word or what the author was saying.
I'm a fluent reader. Heck reading has been a primary hobby since I was a little kid. But also I'm from a Caribbean country where English is the official, but not the native language. And I've been studying/exposed to Japanese for quite a few years. Maybe that diversity/stretch makes a difference.
> …it’s only fluent readers who have trouble.<p>That makes sense, because fluent readers are using more System 1 processing to read - they’re not thinking about it for the most part, they’re just pattern matching instinctively based on text they’ve been exposed to previously (“trained on”). AI writing breaks those patterns so compromises their fluency.<p>Whereas a second language reader or poor reader is exerting more conscious attention to understand the text, so it doesn’t matter as much if it doesn’t conform to standard patterns.
Except LLMs are "System I" <i>writers.</i> You'll be more satisfied with what they've written if you haven't really understood it and just let it wash over you.<p>It only falls apart when you actually try to figure out what's being said.<p>I find the most passionate advocates of LLMs right now to be the people who are<p>1) also the most passionate advocates of not reading or reviewing their output in any detail. They seem to be actively annoyed by the idea that people think they should. And<p>2) people who have a strange theory of mind about LLMs that excuses the low quality of their output. I've heard many times people advancing the theory that LLMs are coding in a particular way because the way that LLMs code is the way that they (the LLMs themselves!) are best able to work with the code.<p>In fact, I get the impression from these people that they think that their touching the code would actually ruin this delicate balance that the LLM is trying to make. This is actually <i>crazy</i> thinking. On a pretty obvious level, they already think that any LLM they encounter is a super-intelligent God acting in mysterious ways, and that they only way you can ruin its plan is by questioning it.
What's the test for understanding? I find that I know what all the words mean, just like in Markov chain text. In LLM text I also know what the sentences mean. In both cases I don't always know what the document means.<p>This is likely relative to my existing knowledge. Humans are also LLMs, and a human without prior training will be able to transfer-learn slop from a computer LLM, but a human with prior training may find the attempt at transfer-learning conflicts with what they have already learned, and reject it.<p>In less ML terms I think a human who reads slop and has no existing knowledge of the subject will just learn the slop by rote and confuse it for actual knowledge.
> What's the test for understanding?<p>Dunno about any test, but I get the meaning behind the words in a way that I can take appropriate action (as someone using LLMs heavily for dev and always following the transcript so I can properly steer them). And often I see people here screaming "AI text" or similar regarding some linked article, and having read it I found that I just bypassed all the "tells" without realizing, because they didn't affect my absorption of the meaning of the article. Makes me think people are consciously looking for those tells so they can scream and shake their pitchforks.