LLMs can get "brain rot"

https://llm-brain-rot.github.io/

by tamnd • 9 months ago

473 293 9 months ago

llm-brain-rot.github.io

AznHisoka2• 9 months ago

Can someone explain this in laymen terms?

PaulHoule2• 9 months ago

They benchmark two different feeds of dangerous tweets:

  (1) a feed of the most popular tweets based on likes, retweets, and such
  (2) an algorithmic feed that looks for clickbait in the text

and blend these in different proportions to a feed of random tweets that are not popular nor clickbait and find that feed (1) has more of damaging effect on the performance of chatbots. That is, they feed that blend of tweets into the model and then they ask the models to do things and get worse outcomes.

ForHackernews• 9 months ago

Blended in how? To the training set?

sailingparrot2• 9 months ago

train on bad data, get a bad model

xpe• 9 months ago

train on bad data, get a bad model

Right: in the context of supervised learning, this statement is a good starting point. After all, how can one build a good supervised model if you can't train it on good examples?

But even in that context, it isn't an incisive framing of the problem. Lots of supervised models are resilient to some kinds of error. A better question, I think, is: what kinds of errors at what prevalence tend to degrade performance and why?

Speaking of LLMs and their ingestion processing, there is a lot more going on than purely supervised learning, so it seems reasonable to me that researchers would want to try to tease the problem apart.

rriley4• 9 months ago

The study introduces the "LLM Brain Rot Hypothesis," asserting that large language models (LLMs) experience cognitive decline when continuously exposed to low-quality, engaging content, such as sensationalized social media posts. This decline, evident in diminished reasoning, long-context understanding, and ethical norms, highlights the critical need for careful data curation and quality control in LLM training. The findings suggest that standard mitigation strategies are insufficient, urging stakeholders to implement routine cognitive health assessments to maintain LLM effectiveness over time.

TL;DR from https://unrav.io/#view/8f20da5f8205c54b5802c2b623702569

RobMurray• 9 months ago

6 7

pixelmelt• 9 months ago

Isn't this just garbage in garbage out with an attention grabbing title?

philipallstar• 9 months ago

Attention is all you need.

echelon3• 9 months ago

In today's hyper saturated world, attention is everything:

- consumer marketing

- politics

- venture fundraising

When any system has a few power law winners, it makes sense to grab attention.

Look at Trump and Musk and now Altman. They figured it out.

MrBeast...

Attention, even if negative, wedges you into the system and everyone's awareness. Your mousey quiet competitors aren't even seen or acknowledged. The attention grabbers suck all the oxygen out of the room and win.

If you go back and look at any victory, was it really better solutions, or was it the fact that better solutions led to more attention?

"Look here" -> build consensus and ignore naysayers -> keep building -> feedback loop -> win

It might not just be a societal algorithm. It might be one of the universe's fundamental greedy optimization algorithms. It might underpin lots of systems, including how we ourselves as individuals think and learn.

Our pain receptors. Our own intellectual interests and hobbies. Children learning on the playground. Ant colonies. Bee swarms. The world is full of signals, and there are mechanisms which focus us on the right stimuli.

dormento• 9 months ago

In case anyone missed the reference: https://arxiv.org/abs/1706.03762

(...) We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely.

wat10000• 9 months ago

Considering that the current state of the art for LLM training is to feed it massive amounts of garbage (with some good stuff alongside), it seems important to point this out even if it might seem obvious.

CaptainOfCoit2• 9 months ago

I don't think anyone is throwing raw datasets into LLMs and hoping for high quality weights anymore. Nowadays most of the datasets are filtered one way or another, and some of them highly curated even.

Barrin92• 9 months ago

Yes, I am concerned about the Computer Science profession

"“Brain Rot” for LLMs isn’t just a catchy metaphor—it reframes data curation as cognitive hygiene for AI"

A metaphor is exactly what it is because not only do LLMs not possess human cognition, there's certainly no established science of thinking they're literally valid subjects for clinical psychological assessment.

How does this stuff get published, this is basically a blog post. One of the worse aspects of the whole AI craze is that is has turned a non-trivial amount of academia into a complete cargo cult joke.

bpt3• 9 months ago

It is a blog post, it was published as a Github page and on arXiv.

I think it's intended as a catchy warning to people who are dumping every piece of the internet (and synthetic data based on it!) that there are repercussions.

jll292• 9 months ago

How does this stuff get published

"published" only in the sense of "self-published on the Web". This manuscript has not or not yet been passed the peer review process, which is what scientist called "published" (properly).

otterley• 9 months ago

And with extra steps!

Insanity• 9 months ago

Garbage in -> Magic -> Hallucinated Garbage out

icyfox• 9 months ago

Yes - garbage in / garbage out still holds true for most things when it comes to LLM training.

The two bits about this paper that I think are worth calling out specifically:

- A reasonable amount of post-training can't save you when your pretraining comes from a bad pipeline; ie. even if the syntactics of the input pretrained data are legitimate it has learned some bad implicit behavior (thought skipping)

- Trying to classify "bad data" is itself a nontrivial problem. Here the heuristic approach of engagement actually proved more reliable than an LLM classification of the content

satellite22• 9 months ago

Yes but the other interesting bit which is not clearly addressed is that increasing the garbage in to 100% does not result in absolute garbage out. So visibly there is still something to learn there.

ashleyn• 9 months ago

Yes, but the idea of chatgpt slowly devolving into Skibidi Toilet and "6 7" references conjures a rather amusing image.

1121redblackgo2• 9 months ago

6-7 ٩(●•)_

CaptainOfCoit2• 9 months ago

continual exposure to junk web text induces lasting cognitive decline in large language models (LLMs).

TLDR: If your data set is junk, your trained model/weights will probably be junk too.

b0gb• 9 months ago

AIs need supervision, just like regular people... /s

thelastgallon• 9 months ago

If most of the content produced by younger generations is about skibidi toilet[1] and 67[2], isn't that what LLMs are going to be trained on?

[1] https://en.wikipedia.org/wiki/Skibidi_Toilet

[2] https://en.wikipedia.org/wiki/6-7_(meme)

micromacrofoot• 9 months ago

only if the trends last long enough (which they rarely do!), skibidi is already old news according to some kids I know

ciaranmca• 9 months ago

Agreed, “ Popularity as a better indicator”. Hypothetically you could look at popularity over time to filter out viral rot content and work out if people feel the content is useful.

Isamu• 9 months ago

Another analogy to help us understand that LLMs are a useful part of what people do but are wildly misconstrued as the whole story

bbstats• 9 months ago

making a model worse is very easy.

moffkalast• 9 months ago

Ah yes, something the local LLM fine tuning community figured out how to do in creative ways as soon as llama 1 released. I'm glad it has a name.

killshotroxs• 9 months ago

If only I got money every time my LLM kept looping answers and telling stuff I didn't even need. Just recently, I was stuck with LLM answers, all while it wouldn't even detect simple syntax errors...

rriley4• 9 months ago

This paper makes me wonder the long lasting effects of the current media consumption patterns by the alpha-gen kids.

AznHisoka2• 9 months ago

why just kids?

rriley4• 9 months ago

I am mostly concerned with the irreversibility part. More developed brains probably would not be affected as much.

avazhi3• 9 months ago

“Studying “Brain Rot” for LLMs isn’t just a catchy metaphor—it reframes data curation as cognitive hygiene for AI, guiding how we source, filter, and maintain training corpora so deployed systems stay sharp, reliable, and aligned over time.”

An LLM-written line if I’ve ever seen one. Looks like the authors have their own brainrot to contend with.

askafriend2• 9 months ago

If it conveys the intended information then what's wrong with that? You're fighting a tsunami here. People are going to use LLMs to help their writing now and forever.

binary132• 9 months ago

The brainrot apologists have arrived

avazhi3• 9 months ago

If you can’t understand the irony inherent in getting an LLM to write about LLM brainrot, itself an analog for human brainrot that arises by the habitual non use of the human brain, then I’m not sure what to tell you.

Whether it’s a tsunami and whether most people will do it has no relevance to my expectation that researchers of LLMs and brainrot shouldn’t outsource their own thinking and creativity to an LLM in a paper that itself implies that using LLMs causes brainrot.

moritzwarhier3• 9 months ago

What information is conveyed by this sentence?

Seems like none to me.

uludag• 9 months ago

Nothing wrong with using LLMs—until every paragraph sounds like it’s A/B tested for LinkedIn virality. That’s the rot setting in.

The problem isn’t using AI—it’s sounding like AI trying to impress a marketing department. That’s when you know the loop’s closed.

stavros3• 9 months ago

The problem is that writing isn't only judged on whether it conveys the intended information or not. It's also judged on whether it does that well, plus other aesthetic criteria. There is such a thing as "good writing", distinct from "it mentioned all the things it needed to mention".

grey-area4• 9 months ago

It’s a text generator regurgitating plausible phrases without understanding and producing stale and meaningless pablum. It doesn’t even know what the intended information is, and judging from the above neither did the human involved.

It doesn’t help writing it stultifies and gives everything the same boring cheery yet slightly confused tone of voice.

AlecSchueler2• 9 months ago

Style is important in writing. It always has been.

sailingparrot2• 9 months ago

If it conveys the intended information then what's wrong with that?

Well, the issue is precisely that it doesn’t convey any information.

What is conveyed by that sentence, exactly ? What does reframing data curation as cognitive hygiene for AI entails and what information is in there?

There are precisely 0 bit of information in that paragraph. We all know training on bad data lead to a bad model, thinking about it as “coginitive hygiene for AI” does not lead to any insight.

LLMs aren’t going to discover interesting new information for you, they are just going to write empty plausible sounding words. Maybe it will be different in a few years. They can be useful to help you polish what you want to say or otherwise format interesting information (provided you ask it to not be ultra verbose), but its just not going to create information out of thin air if you don't provide it to it.

At least, if you do it yourself, you are forced to realize that you in fact have no new information to share, and do not waste your and your audience time by publishing a paper like this.

Angostura• 9 months ago

it’s not really clear whether it conveys an “intended meaning” because it’s not clear whether the meaning - whatever it is - is really something the authors intended.

dwaltrip2• 9 months ago

Because it sounds like shit? Taste matters, especially in the age of generative AI.

And it doesn’t convey information that well, to be honest.

standardly2• 9 months ago

That is indeed an LLM-written sentence — not only does it employ an em dash, but also lists objects in a series — twice within the same sentence — typical LLM behavior that renders its output conspicuous, obvious, and readily apparent to HN readers.

kcatskcolbdi2• 9 months ago

thanks, I hate it.

Jackson__• 9 months ago

LLM slop is not just bad—it's degrading our natural language.

itsnowandnever• 9 months ago

why do they always say "not only" or "it isn't just x but also y and z"? I hated that disingenuous verbosity BEFORE these LLMs out and now it'll all over the place. I saw a post on linked in that was literally just like 10+ statements of "X isn't just Y, it's etc..." and thought I was having a stroke

turtletontine• 9 months ago

I think this article has already made the rounds here, but I still think about it. I love using em dashes! It really makes me sad that I need to avoid them now to sound human

https://bassi.li/articles/i-miss-using-em-dashes

veber-alex• 9 months ago

hehe, I see what you did there.

AlecSchueler2• 9 months ago

Don't forget the "it's not just X, it's Y" formulation and the rule of 3.

hunter-gatherer• 9 months ago

Lol. This is brilliant. I'm not sure if anyone else has this happen to them, but I noticed in college my writing style and "voice" woukd shift quite noticeably depending on whatever I was reading heavily. I wonder if I'll start writing more like an LLM naturally as I unavoidably read more LLM-generated content.

b33j0r2• 9 months ago

I talked like that before this happened, and now I just feel like my diction has been maligned :p

I think it’s because I was a pretty sheltered kid who got A’s in AP english. The style we’re calling “obviously AI” is most like William Faulkner and other turn-of-the-20th-century writing, that bloggers and texters stopped using.

drekipus• 9 months ago

Am I the only one who picks this as LLM output too?

mikeiz4042• 9 months ago

Ah now that's the kind of authentically human response I was hoping for!

(It's a joke: The parent uses the same writing style they described as being indicative of LLMs)

vardump• 9 months ago

Damn, I've used em dash often — do I have to stop using it?

Sigh.

Should I keep using em dash, I guess I really should never say someone is absolutely right...

kragen10• 9 months ago

I've been doing that for decades. See for example https://www.mail-archive.com/kragen-tol@canonical.org/msg000...:

Many programming languages provide an exception facility that terminates subroutines without warning; although they usually provide a way to run cleanup code during the propagation of the exception (finally in Java and Python, unwind-protect in Common Lisp, dynamic-wind in Scheme, local variable destructors in C++), this facility tends to have problems of its own --- if cleanup code run from it raises an exception, one exception or the other, or both, will be lost, and the rest of the cleanup code at that level will fail to run.

I wasn't using Unicode em dashes at the time but TeX em dashes, but I did switch pretty early on.

You can easily find human writers employing em dashes and comma-separated lists over several centuries.

captainclam• 9 months ago

lol

rhubarbtree• 9 months ago

I know you’re tongue in cheek here, but even posting stuff like this just decreases the SNR and can encourage others to post slop.

Psyonic• 9 months ago

well played

mtillman2• 9 months ago

I recently saw someone on HN comment about LLMs using “training” in quotes but no quotes for thinking or reasoning.

Making my (totally rad fwiw) Fiero look like a Ferrari does not make it a Ferrari.

snickerbockers• 9 months ago

I like to call it tuning, it's more accurate to the way they "learn" by adjusting coefficients and also there's no proven similarity between any existing AI and human cognition.

Sometimes I wonder if any second order control system would qualify as "AI" under the extremely vague definition of the term.

mvdtnz• 9 months ago

What is actually up with the "it's not just X, it's Y" cliche from LLMs? Supposedly these things are trained on all of the text on the internet yet this is not a phrasing I read pretty much anywhere, ever, outside of LLM content. Where are they getting this from?

kalavan• 9 months ago

It's probably getting amplified by the RLHF stage because the earlier models didn't do that.

But that just shifts the question to "what kind of reviewer actually likes 'it's not just X' cliche?" I have no idea.

Nio1024• 9 months ago

I think using large language models really accelerates mental atrophy. It's like when you use an input method for a long time, it automatically completes words for you, and then one day when you pick up a pen to write, you find you can't remember how to spell the words. However, the main point in the article is that we need to feed high-quality data to large language models. This view is actually a consensus, isn't it? Many agent startups are striving to feed high-quality domain-specific knowledge and workflows to large models.

malfist• 9 months ago

Also if you've built the perfect filter for context haven't you just built a real ai?

conartist6• 9 months ago

And if they need to keep their own output out of the system to avoid model collapse, why don't I?

There's this double standard. Slop is bad for models. Keep it out of the models at all costs! They cannot wait to put it into my head though. They don't care about my head.

BobbyTables2• 9 months ago

HR people have been speaking that way long before LLMs.

Did you already update and align your OKR’s? Is your career accelerating from 360 degree peer review, continuous improvement, competency management, and excellence in execution? Do you review your goals daily, with regular 1-on-1 discussions with your Manager?

sophiebits• 9 months ago

“360 degree peer review” isn’t a thing, the whole idea is that a 360 includes feedback from both your manager and your peers, that’s what distinguishes it from a 180!

az09mugen2• 9 months ago

It is sad people study "brain rot" for LLMs but not for humans. If people were more engaged in cognitive hygiene for humans, many of the social media platforms would be very sane.

jeltz2• 9 months ago

What do you base your claim on that people don't study that? I do not follow the research in that area but would find it highly unlikely there was no research into it.

zvmaz• 9 months ago

I wish I had your confidence in "detecting" LLM sentences. All I can do for now is get a very vague "intuition" as to whether a sentence is LLM-generated. We know how intuitions are not always reliable.

mortenjorck• 9 months ago

This is pretty clearly an LLM-written sentence, but the list structure and even the em dashes are red herrings.

What qualifies this as an LLM sentence is that it makes a mildly insightful observation, indeed an inference, a sort of first-year-student level of analysis that puts a nice bow on the train of thought yet doesn't really offer anything novel. It doesn't add anything; it's just semantic boilerplate that also happens to follow a predictable style.

ratelimitsteve• 9 months ago

for me it was the word "corpora"

mock-possum• 9 months ago

Plus “X isnt just Y—it’s Z” another usual suspect

mtillman2• 9 months ago

I think it's funny/logical how research suggests LLM use makes the user—who is writing more content for the LLM to consume, of course—less intelligent, which makes the system get less intelligent over time.

Sugar, alcohol, cigarettes, and LLMs.

potsandpans2• 9 months ago

Im curious where all you top commenters were 5 years ago when grammarly was a product used by most professional writers.

If you weren't as incensed then, it's almost like your outrage and compulsion to post this on every hn thread is completely baseless.

ewoodrich2• 9 months ago

Perhaps because it didn't stick out like a sore thumb? Or because it became so prevalent they observe the exact same tics in every other article they read nowadays?

gaogao2• 9 months ago

Brain rot texts seems reasonably harmful, but brain rot videos are often surreal and semantically dense in a way that probably improves performance (such as discussed on this German brain rot analysis https://www.youtube.com/watch?v=-mJENuEN_rs&t=37s). For example, Švankmajer is basically proto-brainrot, but is also the sort of thing you'd watch in a museum and think about.

Basically, I think the brain rot aspect might be a bit of terminology distraction here, when it seems what they're measuring is whether it's a puff piece or dense.

f_devd2• 9 months ago

I do not think this is the case, there has been some research into brainrot videos for children[0], and it doesn't seem to trend positively. I would argue anything 'constructed' enough will not classify as far on the brainrot spectrum.

[0] https://www.forbes.com/sites/traversmark/2024/05/17/why-kids...

gaogao2• 9 months ago

Yeah, I don't think surrealism or constructed is good in the early data mix, but as part of mid or post-training seems generally reasonable. But also, this is one of those cases where anthropomorphizing the model probably doesn't work, since a major negative effect of Cocomelon is kids only wanting to watch Cocomelon, while for large model training, it doesn't have much choice in the training data distribution.

moritzwarhier3• 9 months ago

For this reason, I believe thar the current surge we see in AI use for people manipulation (art is also a form of manipulation, even if unintended) is much more important than their hyped usage as a technical information processors.

Brainrot created by LLMs is important to worry about, their design as "people pleasers".

Their anthropomorphization can be scary too, no doubt.

conception• 9 months ago

This is a potential moat for the big early players in a pre-atomic steal sort of way as any future players won’t have a non-AI-slop/dead internet to train new models on.

andai2• 9 months ago

I encourage everyone with even a slight interest in the subject to download a random sample of Common Crawl (the chunks are ~100MB) and see for yourself what is being used for training data.

https://data.commoncrawl.org/crawl-data/CC-MAIN-2025-38/segm...

I spotted here a large number of things that it would be unwise to repeat here. But I assume the data cleaning process removes such content before pretraining? ;)

Although I have to wonder. I played with some of the base/text Llama models, and got very disturbing output from them. So there's not that much cleaning going on.

throwaway314155• 9 months ago

But I assume the data cleaning process removes such content before pretraining? ;)

I didn't check what you're referring to but yes, the major providers likely have state of the art classifiers for censoring and filtering such content.

And when that doesn't work, they can RLHF the behavior from occurring.

You're trying to make some claim about garbage in/garbage out, but if there's even a tiny moat - it's in the filtering of these datasets and the purchasing of licenses to use other larger sources of data that (unlike Common Crawl) _aren't_ freely available for competition and open source movements to use.

jedimastert• 9 months ago

purchasing of licenses to use other larger sources of data

https://www.npr.org/2025/09/05/g-s1-87367/anthropic-authors-...

dist-epoch• 9 months ago

Karpathy made a point recently that the random Common Crawl sample is complete junk, and that something like an WSJ article is extremely rare in it, and it's a miracle the models can learn anything at all.

andai2• 9 months ago

Turns out that LLMs learn a lot better and faster from educational content as well. This is partly because the average Common Crawl article (internet pages) is not of very high value and distracts the training, packing in too much irrelevant information.
The average webpage on the internet is so random and terrible it's not even clear how prior LLMs learn anything at all. You'd think it's random articles but it's not, it's weird data dumps, ad spam and SEO, terabytes of stock ticker updates, etc. And then there are diamonds mixed in there, the challenge is pick them out.

https://x.com/karpathy/status/1797313173449764933

Context: FineWeb-Edu, which used Llama 70B to [train a classifier to] filter FineWeb for quality, rejecting >90% of pages.

https://huggingface.co/spaces/HuggingFaceFW/blogpost-fineweb...

jojobas• 9 months ago

From the current WSJ front page:

Paul Ingrassia's 'Nazi Streak'

Musk Tosses Barbs at NASA Chie After SpaceX Criticism

Travis Kelce Teams Up With Investor for Activist Campaign at Six Flags

A Small North Carolina College Becomes a Magnet for Wealthy Students

Cracker Barrel CEO Explains Short-Lived Logo Change

If that's the benchmark for high quality training material we're in trouble.

commandlinefan• 9 months ago

My son just sent me an instagram reel that explained how cats work internally, but it was a joke, showing the "purr center" and "knocking things off tables" organ. It was presented completely seriously in a way that any human would realize was just supposed to be funny. My first thought was that some LLM is training on this video right now.

Night_Thastus• 9 months ago

I'm reminded of this 'repair' video: https://www.youtube.com/watch?v=3e6motL4QMc

koakuma-chan• 9 months ago

https://www.youtube.com/watch?v=sZkB11pO9R8

chuckreynolds• 9 months ago

is that why chatGPT always tells me "6 7 lol"? ;)

jdkee• 9 months ago

" Studying “Brain Rot” for LLMs isn’t just a catchy metaphor—it reframes data curation as cognitive hygiene for AI, guiding how we source, filter, and maintain training corpora so deployed systems stay sharp, reliable, and aligned over time."

Is this slop?

Profan• 9 months ago

... it sure reads like slop

and you know what they say, if it walks like slop, quacks like slop and talks like slop, it's probably slop

earth2mars• 9 months ago

duh! isn't that obvious. is this some students wanted a project with pretty graphs on writing experience?! I am not trying to be cynical or anything. just questioning the obvious thing here.

antegamisou3• 9 months ago

My Goodness, looks like Computer 'Science' is a complete euphemism now.

guelo• 9 months ago

It's turning into a social science.

nakamoto_damacy• 9 months ago

Our metaphorical / analogical muscle is too well developed. Maybe there is a drug we can take to reduce how much we lean into it.

If you look at two random patterns of characters and both contain 6s you could say they are similar (because you’re ignoring that the similarity is less than 0.01%). That’s how comparing LLMs to brains feels like. Like roller skates to a cruise ship. They both let you get around.

buyucu• 9 months ago

I don't understand why people have a hard time understanding 'garbage in, garbage out'. If you train your model on junk, then you will have a junk model.

kayodelycaon• 9 months ago

I suspect this is “the computer is always right”.

A lot of people think computers have better answers than people.

AI is just another type of computer. It knows a lot of things and sounds confident. Why wouldn’t it be right?

nomel2• 9 months ago

"Brain rot" is just the new term for "slang that old people don't understand".

"Cool" and "for real" are no different than "rizz" and "no cap". You spoke "brain rot" once, and "cringed" when your parents didn't understand. The cycle repeats.

kcatskcolbdi2• 9 months ago

This both has nothing to do with the linked article (beyond the use of brain rot in the title, but I'm certain you must have read the thing you're commenting on, surely) and is simply incorrect.

Brain rot in this context is not a reference to slang.

nomel2• 9 months ago

Brain rot in this context is not a reference to slang.

I suppose I should have replied to one of the many comments here where my response is the correct context, rather than a top level.

Razengan• 9 months ago

Based on my hobby of collecting old gaming magazines, I think 1980s UK had the best slang.

buellerbueller• 9 months ago

By all means, let's make sure the LLMs have healthier media diets than the humans. We wouldn't want the humans to realize they are being dumbed down into cattle. /s

themafia• 9 months ago

as cognitive hygiene

LLMs are not cognizant. It's a terrible metaphor. It hides the source of the issue. The providers cheaped out on sourcing their data and now their LLMs are filled with false garbage and copyrighted material.

donaldihunter• 9 months ago

Likewise, cognitive decline isn't what's happening here since that would require cognition. At best it is a simulation of cognitive decline.

numpad0• 9 months ago

Not surprising that trending tweets as data is junk, not only from brainrots-be-brainrots perspective: trending tweets are contextual. They don't make sense without the rest of the timeline.

And now I know why bots on Twitter don't even work, even with humans in it - they're shooting blind.

dankai• 9 months ago

Naive question: Whats new about the finding that data quality matters when training an LLM?

donkeylazy456• 9 months ago

can't wait LLM says "tung x9 sahur" without any context.

snorbleck• 9 months ago

interesting, since trivial or unchallenging online content rots actual brains too!

skopje• 9 months ago

"Trivial or Unchallenging Content" (points to Twitter). I love it.

alexchantavy• 9 months ago

Off topic completely but I really like the font used in this blog

liqilin1567• 9 months ago

I like the gradient font style in the overview :)

f4uCL9dNSnQm• 9 months ago

I hate it but I still found it interesting that something like it can be done with just CSS (-webkit-linear-gradient)

Edit: I noticed that replacing it with "standard" "linear-gradient" reverses the direction of gradient.

Version4672• 9 months ago

So they trained LLM's on a bunch of junk and then notice that it got worse? I don't understand how that's a surprising, or even interesting result?

nazgul17• 9 months ago

They also tried to heal the damage, to partial avail. Besides, it's science: you need to test your hypotheses empirically. Also, to draw attention to the issue among researchers, performing a study and sharing your results is possibly the best way.

yieldcrv2• 9 months ago

I don’t understand, so this is just about training an LLM with bad data and just having a bad LLM?

just use a different model?

dont train it with bad data and just start a new session if your RAG muffins went off the rails?

what am I missing here

Version4672• 9 months ago

Yeah I mean I get that, but surely we have research like this already. "Garbage in, garbage out" is basically the catchphrase of the entire ml field. I guess the contribution here is that "brainrot"-like text is garbage which, even though it seems obvious, does warrant scientific investigation. But then that's what the paper should focus on. Not that "LLMs can get 'brain rot'".

I guess I don't actually have an issue with this research paper existing, but I do have an issue with its clickbait-y title that gets it a bunch of attention, even though the actual research is really not that interesting.

Perz1val• 9 months ago

I seen claims that you can train the models with anything, so it would be a research to check that

Sxubas• 9 months ago

Sometimes the simplest of experiments/observations can lead to useful results: You can't do science without challenging your beliefs.

And while this result isn't extraordinary, it definitely creates knowledge and could close the gap to more interesting observations.

Rileyen• 9 months ago

After reading this, I just felt like everyone already knows the data is a mess, but no one really cares. We feed the models a bunch of junk, then act surprised when they start getting dumber. Honestly, did we even need a study to figure that out?

mikeiz4042• 9 months ago

"brain rot", "Thought-skipping", "primary lesion", "Cognitive Declines", ...

In general using these medical/biological metaphors doesn't seem like a good idea in things like computer science research papers and similar.

Their use forces many inaccurate comparisons (when compared in detail) and they engender human qualities to what are already forgotten to be just computer models. I get this may be done with a slight tongue-in-cheek but with research papers there is also the risk that these terms start to be adopted. And undoing that would be a much taller order in either the research community or general media.

Maybe I am just yelling at clouds.

Animats• 9 months ago

The two big problems listed:

* Thought-skipping as the primary lesion: models increasingly truncate or skip reasoning chains, explaining most of the error growth.

* Popularity as a better indicator: the popularity, a non-semantic metric, of a tweet is a better indicator of the Brain Rot effect than the length in M1.

That's what you'd expect. Popular culture content tends to jump from premise to conclusion without showing the work. Train on popular culture and you get that. Really, what's supposed to come from training on the Twitter firehose? (Can you still buy that feed? Probably not.) This is a surprise-free result.

At least have a curated model (no social media) and a junk model to compare.

dee_s101• 9 months ago

Is it just me or has GPT5 turned into a bit of a donkey?

nathias• 9 months ago

spoken as a true LLM

loloquwowndueo• 9 months ago

Tralalero tralala

gamerrk• 9 months ago

So LLMs are indeed human like

b800h• 9 months ago

It's like showing modern children's TV to kids.

ederamen• 9 months ago

LLMs are brain rot

devin• 9 months ago

Speaking of, someone shared with me an AI financial challenge where they are pitting the LLMs against one another to make trades, manage risk, etc. and then track their performance against one another with starting capital of 10,000 USD each.

Their starting portfolios are ludicrous. They are trading BTC, XRP, DOGE, etc. I thought the idea was somewhat interesting, but then I felt like the only reasonable takeaway I had was that these models have intense brainrot from consuming twitter, reddit, etc. and as such have a completely warped view of "finance".

3Sophons• 9 months ago

why they don't prompt ai to avoid using dashes. and bullet points etc

LLMs can get "brain rot"

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