A model trained to minimize statistical prediction errors is going to sound ... predictable (aka boring). A (good) human author tries to keep their writing interesting, while an LLM strives for predictable instead.

It's not just predicting content but also the form/shape of the response, highly shaped by RLHF preferences, which seems to encourage longer form responses which need padding with filler to generate the "preferred" amount of verbiage.

It seems most of these models have been trained to interpret questions as wanting a long-form authoritative/comprehensive-sounding response (even when midwit filler is all it has to offer), rather than initiating a short-turn conversation which would better allow you to steer the conversation. Apparently Muse is much better in this regard, but so far I've resisted signing up for any Meta/FaceBook service, and not inclined to break this winning streak!

Don't these sort of conflict with being "aggressively pro AI"?

> how awful everything is to read lately > flooded with AI generated content, all of it terrible > everything sounding identical and everything being a rambling unconcise mess.

I can see the benefits of AI and enjoy using the technology - but, like many have said, it's wreaking havoc on the "content" that's out there nowadays. Gotta take some bad with the good, I suppose.

Agree but also want to point out that this started with the SEO boom of the last decade where sites started rambling so they would keep you on the site longer and get more SEO "points", they were articles meant for a robot to catch humans.

AI has made it even worse and lots of content on the web, for both these reasons, is not worth you time (now wonder users don't read most of the text on our apps!)

My model is about gaps - all ideas start with gaps. LLMs can fill in these gaps quickly - but some ideas are just bad - so the filling in does not improve them. Maybe it would be useful to treat that filling in as a kind of hallucination - the LLM trying to meed impossible requirements. But on other cases the ideas are good and the LLMs fill in the gaps in the right way. It is a kind of skill to see when LLMs are hallucinating in the gaps. I use LLMs very extensively and I am adding checks to that should catch when the filling in is weak - but it is still not fully autonomous.

I also try to keep the llm-enhanced (or even fully llm-generated) content separate from my human writing - because it is true that the failure modes are different. But think LLM writing might be useful even if it is not consumed directly - it can enhance LLM answering your questions.

I suspect it’s partially an average / regression to mean.

If you built a model only based on long form journalism and The New Yorker voice, including both the template structure of the article and the grammar and syntax, the model would replicate the patterns it is built upon. You could do the same for high vocabulary rap.

When you stuff all of English or all of human thought into it with somewhat equal weight, including very technical writing the sum is a lack of style and elegance and personality.

The second problem is that writing often doesn’t happen linearly. You write bookends and fill in, edit and revise. You sort of know where you’re going and can then make decisions how to get there. Language models may benefit from being redesigned to perform passes. Select a template of some sort, fill in the mad lib, then run a detail pass, then run a persuasion / emotion pass, then run a style pass. Current model writing very much sounds like I would expect autocomplete to.

The part I find grating is the attempts to punch everything up. ("It's not X. It's Y"). Like, its a react badge component, it doesn't need punching up. You didn't find the smoking gun in a murder mystery, just a misplaced CSS rule.

Sorry, meant to reply to your comment, but my post above...

"You can be pro AI productivity but anti-AI creativity. Automating creativity is the death of art."

Meaning you reject the creative value of AI but values it's ability to drive a car safely, or write code for your job, or turn off the lights when you leave the room. objective goal oriented task completion great.

But AI draws your a picture? What's the point. AI writes you a song? No feeling. AI creativity isn't interesting.

> sounding identical

In AI-generated entertainment, the names "Marcus", "Chloe", "Sterling", "Meridian", and "Apex" keep appearing. Those aren't common names, but LLMs seem to prefer them.

I’ve got the same bright red line for 2023. E.g. I treat a handful of youtube channels with long electronic jams almost like oldies at this point - keep listening to the same stuff from at least two years ago because it’s the authentically good stuff.

Maybe stop being pro ai then.

It's (at least) two things I think; one is that thanks to the internet, the ability to focus on things for long is shot - I struggle to read long form content on the internet, generally speaking. I also did my whole career with Google and skim reading documentation looking for solutions for an immediate problem more than getting a wholesale end-to-end understanding.

(this isn't a new observation / thing, see https://randsinrepose.com/archives/nadd/ originally from somewhere in the 2000s)

But now with AI it's easy to generate a lot of prose. I'm thinking for example of the merge request descriptions my colleague submits. And here's the thing, it's not even bad or incorrect - I read it thinking, if I were to really sit down for it, get into The Zone etc, I could crank this out myself.

But I don't because honestly the MR is relatively trivial and can be described in just a sentence or two. Style and accuracy aside, my current take is that LLMs write things that humans wouldn't bother with, because it's too much effort and there's not enough value.

I mean it's an MR for an enterprise library project, the chances that anyone will ever read that MR description again once it's merged is close to zero.

That said: LLMs themselves don't get that, they will read everything you tell them to with the same amount of attention. Granted, their context windows are limited (and performance declines the more you put in it), so they too will use some tools to skim and pick out the important parts, but I think that's a temporary thing. The huge amount of LLM prose will end up being consumed by other LLMs. I don't know enough about the subject whether that's a good or a bad thing though, right now I think it just adds to the noise until it becomes noise in, noise out.

Do you have some personal financial or other incentive to be “aggressively pro AI”?

> it's a mixture of everything sounding identical and everything being a rambling unconcise mess.

For me it's mostly the latter, which is a natural consequence of LLM writing. When the (time) cost-per-word goes down, there is less incentive to keep signal-to-noise ratio high, so it ends up verbose and bloated. I also believe that this is (at least partially) upstream from everything sounding identical: take two articles on two different subjects - when SNR is high, there isn't really much room for them to become similar (The ingredient list for a risotto will never read the same as the ingredient list for pizza), but when 80% of them is fluff, they can blend together more easily (To take the recipe comparison, this would be the background/story part above the recipe), especially when written by the same LLM

LLMs have made the internet unreadable. It used to be just Google search results were SEO-gamed empty prose, but now it's every blog post, news article, product announcement ... everything. I've stopped reading basically everything online except user comments on a few websites. It's the last place remaining to actually connect with other human minds, but even that seems like it won't last forever.

My guess is that tokenization is typically not (strictly) syllabic.

OP’s linked article mentions the importance for them of reading their writing aloud. Humans construct sentences with a (mostly unconscious) sonic influence. How things sound really matters.

My theory is that LLM prose sounds slop-like because the base unit for an LLM is not a single sound.

Thus, perhaps, you get the uncanny, meandering, hollow-feeling outputs, which feel vapid to human readers, without the reader really knowing quite why.

I think this is also part of the reason why LLMs are rewarded for and thus more strongly exhibit their faux-punchy constructions, such as the infamous “it’s not [X], it’s [Y].” The transformer model can, with current tokenization, articulate some features of “good writing”, but not quite all.

I think there are a number of fairly simple SOLNs to this (or ways to test this hypothesis) without making tokenizers purely syllabic across the board, which would probably be a mistake.

Whether prose output quality even matters for models which are not explicitly trained for creative writing is highly debatable, though.

This applies to code btw, it's so much nicer to read a human made pull requests. It's also much faster as people tend to be lazy and produce denser code.

Honestly I think it's a good thing it's so hard to get AI to talk normal and I hope they keep making the voice more and more distinct. Agents should run as background tasks and automations. They should be understandable so they can communicate with you about what they did, and for observability/ai safety. Im perfectly fine reading llm speak when it's an ai telling me what work it did. When in reading a blog post with a human listed as the author though, I do not want to read an AIs thoughts that the human is laundering. Honestly I'd probably tolerate it if it was framed as "I asked my Claude to do a cool thing here is its report on what it did." But in general it's really nice to have an instant filter of - this post is a waste of time - ah this post has an actual human's thoughts

This is true across every topic. Anything at all that someone can put into an LLM and get out an emoji-laiden wikipedia article sized post to slap on a heavily SEO-ed website is absolutely flooded with it.

My parents live on a rural property, and I'll help them do research on things like livestock practices or occasionally "prepper" related things to give them a sanity check before they spend money. I have to weed through a dozen or more obvious ChatGPT-created pages that are the first page or two of results on every search engine before I get to anything a human has touched. The vast majority of the LLM-created pages are either massively verbose and vague, or often wrong. But they're getting clicks. Very often, when asking an LLM the same question and to give citations, I'll get a combination of useless, irrelevant sites with no connection to the material, maybe one of these LLM-generated sites, and maybe one online forum.

I'm not aggressively pro-AI, I'm in the middle, but stuff like this has me worried that we're looking at the equivalent of the Dark Ages all over again. But instead of simply no one bothering to write anything down as was the case in the early Middle Ages, it's going to be the task of searching for real human work and thought in a sea of aggressively spammed LLM-generated slop pages.

to AI verbosity and weird use of diction is natural, to humans it makes it hard to parse. The number of time i start reading long winded AI written design docs, and i just can't get into the flow after reading a few sentences and have to keep re reading certain sentences.

How is someone "aggressively pro AI"? This is the first time I read such concept.

Personally, for blog posts, documentation (unless I flat out vibe code the entire thing) etc are all written by me by hand. If the model can save me hours of "oops that was really dumb of me" why can't I take time to read and document things by hand? I think everyone's embracing AI in the wrong ways. Maintain your knowledge of things, and keep the documents nice. There's still room for letting your agent manage agent specific docs, but for the humans, just write it yourself, ask the model for feedback and tweak it by hand. Let the machine talk to the machines.

Gentle reminder that most AI token spend is used for TPS report type writing not Cutting edge fiction writing. Creative writing, or general human-to-human communication was never really the target. It's not surprising the first majority use of this was code.

Endless headlines like, "aArON SORkIn SAYs hE WiLL Never UsE ai to WRite" doesn't change that base fact. No one asked him to, other than clickbait journalists.

If you find this hard to believe, just think about relative market sizes.

100 percent agree. It's like the consensus for tone and vocabulary has been "midlevel digital marketer from 2015" and now everything is starting to sound like a lazy sales pitch. I'm with you on AI but man, this is such an annoying side effect.

> I've also noticed how awful everything is to read lately.

Yeah, thanks dude. We appreciate your efforts to enshittify everything.

Edit: Sure, downvote, but the parent comment is really not very different from "arsonist puzzled and upset that so many buildings have burned down".

And yet you remain aggressively pro AI? Why?

So, you're still aggressively pro AI even though you recognize how quickly it is destroying the quality of online content?

I've noticed this a lot in conservative spaces especially, when they say "I love Trump, but..." -- why the need to say how much you love AI before criticizing it? Who are you trying to convince?

I use LLMs constantly for work and they have genuinely improved my life. But I absolutely despise generated "content". Text, audio, video, images -- for the reasons you describe.

It's ok to have a nuanced and multi-faceted opinion and approach to this and not have to call yourself a raving fanboy.