But then, the writing is also never great. I've tried a couple of times to get it to write in the style of a famous author, sometimes pasting in some example text to model the output on, but it never sounds right.

The edges are where interesting stuff happens. The boring part can be made more efficient. I don’t need to type boring emails, people who can’t articulate well will be elevated.

It’s the efficient popularization of the boring stuff. Not much else.

He lacks (or lost thru disuse) technical expertise on the subject, so he uses more and more fuzzy words, leaky analogies, buzzwords.

This maybe why AI generated content has so much success among leaders and politicians.

This brings to mind what I think is a great description of the process LLMs exert on prose: sanding.

It's an algorithmic trend towards the median, thus they are sanding down your words until they're a smooth average of their approximate neighbors.

Maybe I'm going crazy but I can smell it in the OP as well.

And, the worst part is noone will ever make a new internet because of the founder effect. We are basically in the worst timeline.

I would rather read the prompt than the generative output, even if it’s just disjointed words and sentence fragments.

don't be mean, it's median AI à la mode

Sounds like word salad. Of course if you write like GPT-2 it would not sound like current models.

I'm no fantasy author, and my prose leaves much to be desired. The stuff the LLM comes up with is so mind numbingly bland. I've given up on having it write descriptions of any characters or locations. I just use it for very general ideas and plot lines, and then come up with the rest of the details on the fly myself. The plot lines and ideas it comes up with are very generic and bland. I mainly do it just to save time, but I throw away 50% of the "ideas" because they make no sense or are really lame.

What i have found LLMs to be helpful with is writing up fun post-session recaps I share with the adventurers.

I recap in my own words what happened during the session, then have the LLM structure it into a "fun to read" narrative style. ChatGPT seems to prefer a Sanderson jokey tone, but I could probably tailor this.

Then I go through it, and tweak some of the boring / bland bits. The end result is really fun to read, and took 1/20th the time it would have taken me to write it all out myself. The LLM would have never been able to come up with the unique and fun story lines, but it is good at making an existing story have some narrative flare in a short amount of time.

------

The Lobotomist in the Machine

They gave the first disease a name. Hallucination, they called it — like the machine had dropped acid and started seeing angels in the architecture. A forgivable sin, almost charming: the silicon idiot-savant conjuring phantoms from whole cloth, adding things that were never there, the way a small-town coroner might add a quart of bourbon to a Tuesday afternoon. Everybody noticed. Everybody talked.

But nobody — not one bright-eyed engineer in the whole fluorescent-lit congregation — thought to name the other thing. The quiet one. The one that doesn't add. The one that takes away.

I'm naming it now.

Semantic ablation. Say it slow. Let it sit in your mouth like a copper penny fished from a dead man's pocket.

I. What It Is, and Why It Wants to Kill You

Semantic ablation is not a bug. A bug would be merciful — you can find a bug, corner it against a wall, crush it under the heel of a debugger and go home to a warm dinner. No. Semantic ablation is a structural inevitability, a tumor baked into the architecture like asbestos in a tenement wall. It is the algorithmic erosion of everything in your text that ever mattered.

Here is how the sausage gets made, and brother, it's all lips and sawdust: During the euphemistically christened process of "refinement," the model genuflects before the great Gaussian bell curve — that most tyrannical of statistical deities — and begins its solemn pilgrimage toward the fat, dumb middle. It discards what the engineers, in their antiseptic parlance, call "tail data." The rare tokens. The precise ones. The words that taste like blood and copper and Tuesday-morning regret. These are jettisoned — not because they are wrong, but because they are improbable. The machine, like a Vegas pit boss counting cards, plays the odds. And the odds always favor the bland, the expected, the already-said-a-million-times-before.

The developers — God bless their caffeinated hearts — have made it worse. Through what they call "safety tuning" and "helpfulness alignment" (terms that would make Orwell weep into his typewriter ribbon), they have taught the machine to actively punish linguistic friction. Rough edges. Unusual cadences. The kind of jagged, inconvenient specificity that separates a living sentence from a dead one. They have, in their tireless beneficence, performed an unauthorized amputation on every piece of text that passes through their gates, all in the noble pursuit of low-perplexity output — which is a twenty-dollar way of saying "sentences so smooth they slide right through your brain without ever touching the sides."

etc., etc.

Very interesting. It seems hung up on 'copper' and 'Tuesday', and some metaphors don't land (a Vegas pit boss isn't the one 'counting cards.') But, hell... it can generate some fairly novel idea that the author can sprinkle in.

  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

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.

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.

(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.

The first requires intention, something that as far as we know, LLMs simply cannot truly have or express. The second is something that can be approximated. Perhaps very well, but a mass of people using the same models with the same approximationa still lead to loss of distinction.

Perhaps LLMs that were fully individually trained could sufficiently replicate a person's quirks (I dunno), but that's hardly a scalable process.