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?
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?
> 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 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:
process waits for input, a method threw an exception, server accepts the request, Ahem: `SimpleBeanFactoryAwareAspectInstanceFactory` 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.
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.
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" writers. You'll be more satisfied with what they've written if you haven't really understood it and just let it wash over you.
It only falls apart when you actually try to figure out what's being said.
I find the most passionate advocates of LLMs right now to be the people who are
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
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.
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 crazy 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.
> I find the most passionate advocates of LLMs right now ...
Whether or not that's true, you have to keep in mind that it's not true of everyone.
I'm a passionate advocate of LLMs. I implemented my first neural net, for image recognition, in the late 1980s (based on Bart Kosko's work, I'm not claiming to have invented anything.) Now, I use LLMs daily for coding and other tasks. I don't usually "vibe code" unless it's a simple enough project that the code quality really doesn't matter - a simple example would be data integration, where as long as the data in the target system matches the data in the source system, I don't care much about how the sausage in between is made.
But otherwise, I'm approving or denying every tool call, like a caveman, and when they mess up, I explain to them why. I interrupt them and redirect them when they need it. I change code myself and let them know. With all that, they're still incredibly useful - more useful than most "junior programmers", realistically. No human software developer can hope to match LLMs on many significant dimensions of development. But that doesn't mean experienced and competent humans have no role, at least currently.
The disconnect here is that your characterization of "the low quality of their output" is too simplistic. The quality of their natural language writing is different, for the kinds of reasons stated in the OP. Some of the accusations of "lower quality" really boil down to "too compressed for humans to easily understand", again as the OP points out. And the quality of their code has a lot to do with how they're prompted.
One of the biggest barriers to good LLM output - both writing and code generation - is that they're trained on data generated by humans who fit George Carlin's observation that half of them are stupider than average. When it comes to coding, on average they don't have enough education not to think that imperative code with minimally-restricted mutation all over the place is a good idea. They think dynamic typing, so that code can't be statically verified, is a good idea. They reflect our own collective limitations. If you recognize that and redirect them to sources of good practices, they can be much better.
In short, they're much more of a mirror than most people realize or admit.
With all this noise, it should not be forgotten that we appear to have figured out roughly how a significant component of intelligence works, and have real life, useful proofs of concept. All the technical criticism, to me, is just coming from people who don't seem to understand how intellectual advancement works. The political criticism is another matter.
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.
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.
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?
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.
Can you provide an example of an action you would take that would prove you understood a text?