Have you not seen it any time you put any substantial bit of your own writing through an LLM, for advice?

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.

> If you're looking for something beyond corporate safespeak

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.

If you tell me "no fucking way" by running it through an LLM, I will be far more pissed than if you had just sent me "no fucking way". At least in that case I know a human read and responded rather than thinking my email was just being processed by a damned robot.

Yeah but does it make sense to have invested all this money for this?

Lol no. Might be great for you as a consumer who is using these products for free. But expand the picture more.

> Yeah but does it make sense to have invested all this money for this?

No, but it's here. Why wouldn't I use it?

> If you're looking for something beyond corporate safespeak or stylistic pastiche, they drain the blood out of everything.

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.

The prompt I use for proof-reading has worked great for me so far:

  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 links

Kaffee: Corporal, would you turn to the page in this book that says where the mess hall is, please?

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.

> Yet we're not seeing any collapse, despite models being trained mainly on synthetic data for the past 2 years.

Maybe because researchers learned from the paper to avoid the collapse? Just awareness alone often helps to sidestep a problem.

No one did what the paper actually proposed. It was a nothing burger in the industry. Yet it was insanely popular on social media.

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.

Who cares? This is a place where you should be putting forth your own perspective based on your own experience. Not parotting what someone else already wrote.

Yeah, reading the other comments on this thread this is a classic example of that Hacker News (and online forums in general) thing where people jump on the chance to talk about a topic driven purely by the headline without engaging with the actual content.

(I'm frequently guilty of that too.)

Even if that isn't the case, isn't it the fact the AI labs don't want their models to be edgy in any creative way, choose a middle way (buddhism) so to speak. Are there AI labs who are training their models to be maximally creative?

Look through my comment history at all the posts where I complain the author might have had something interesting to say but it's been erased by the LLM and you can no longer tell what the author cared about because the entire post is a an oversold monotone advertising voice.

https://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

https://news.ycombinator.com/item?id=46661936#46663362

https://news.ycombinator.com/item?id=46748077#46749699

I just sent TFA to a colleague of mine who was experimenting with llm's for auto-correcting human-written text, since she noticed the same phenomenon where it would correct not only mistakes, but slightly nudge words towards more common synonyms. It would often lose important nuances, so "shun" would be corrected to "avoid", and "divulge" would become "disclose" etc.