(Not necessarily disagreeing with those claims, but I'd like to see a more robust exploration of them.)
I disagree pretty strongly with most of what an LLM suggests by way of rewriting. They're absolutely appalling writers. If you're looking for something beyond corporate safespeak or stylistic pastiche, they drain the blood out of everything.
The skin of their prose lacks the luminous translucency, the subsurface scattering, that separates the dead from the living.
AI has been great for removing this stress. "Tell Joe no f'n way" in a professional tone and I can move on with my day.
Lol no. Might be great for you as a consumer who is using these products for free. But expand the picture more.
No, but it's here. Why wouldn't I use it?
Strong agree, which is why I disagree with this OP point:
“Stage 2: Lexical flattening. Domain-specific jargon and high-precision technical terms are sacrificed for "accessibility." The model performs a statistical substitution, replacing a 1-of-10,000 token with a 1-of-100 synonym, effectively diluting the semantic density and specific gravity of the argument.”
I see enough jargon in everyday business email that in the office zero-shot LLM unspoolings can feel refreshing.
I have "avoid jargon and buzzwords" as one of very tiny tuners in my LLM prefs. I've found LLMs can shed corporate safespeak, or even add a touch of sparkle back to a corporate memo.
Otherwise very bright writers have been "polished" to remove all interestingness by pre-LLM corporate homogenization. Give them a prompt to yell at them for using 1-in-10 words instead of 1-in-10,000 "perplexity" and they can tune themselves back to conveying more with the same word count. Results… scintillate.
You are a proof reader for posts
about to be published.
1. Identify for spelling mistakes
and typos
2. Identify grammar mistakes
3. Watch out for repeated terms like
"It was interesting that X, and it
was interesting that Y"
4. Spot any logical errors or
factual mistakes
5. Highlight weak arguments that
could be strengthened
6. Make sure there are no empty or
placeholder linkshttps://news.ycombinator.com/item?id=46583410#46584336
https://news.ycombinator.com/item?id=46605716#46609480
https://news.ycombinator.com/item?id=46617456#46619136
https://news.ycombinator.com/item?id=46658345#46662218
https://news.ycombinator.com/item?id=46630869#46663276
https://news.ycombinator.com/item?id=46656759#46663322
Cpl. Barnes: Well, Lt. Kaffee, that's not in the book, sir.
Kaffee: You mean to say in all your time at Gitmo, you've never had a meal?
Cpl. Barnes: No, sir. Three squares a day, sir.
Kaffee: I don't understand. How did you know where the mess hall was if it's not in this book?
Cpl. Barnes: Well, I guess I just followed the crowd at chow time, sir.
Kaffee: No more questions.
It has all the tropes of not understanding the underlying mechanisms, but repeating the common tropes. Quite ironic, considering what the author's intended "message" is. Jpeg -> jpeg -> jpeg bad. So llm -> llm -> llm must be bad, right?
It reminds me of the media reception of that paper on model collapse. "Training on llm generated data leads to collapse". That was in 23 or 24? Yet we're not seeing any collapse, despite models being trained mainly on synthetic data for the past 2 years. That's not how any of it works. Yet everyone has an opinion on how bad it works. Jesus.
It's insane how these kinds of opinion pieces get so upvoted here, while worth-while research, cool positive examples and so on linger in new with one or two upvotes. This has ceased to be a technical subject, and has moved to muh identity.
(I'm frequently guilty of that too.)
Maybe because researchers learned from the paper to avoid the collapse? Just awareness alone often helps to sidestep a problem.
Same with the "llms don't reason" from "Apple" (two interns working at Apple, but anyway). The media went nuts over it, even though it was littered with implementation mistakes and not worth the paper it was(n't) printed on.