Semantic ablation: Why AI writing is generic and boring

Going off search results, it seems to be a new coinage. I found mostly references to TFA, along with an (ironically obviously AI-written) guide with suggestions for getting LLMs to avoid the issue (just generic "traditional" advice for tuning their output, really). The guide was apparently published today, and I imagine that it's a deliberate response to TFA. But FWIW the term "semantic ablation" does seem to me like something that newer models could invent

At any rate, it seems to me like a reasonable label for what's described:

> Semantic ablation is the algorithmic erosion of high-entropy information. Technically, it is not a "bug" but a structural byproduct of greedy decoding and RLHF (reinforcement learning from human feedback).

> ...

> When an author uses AI for "polishing" a draft, they are not seeing improvement; they are witnessing semantic ablation.

The metaphor is very apt. Literal polishing is removal of outer layers. Compared to the near-synonym "erosion", "ablation" connotes a deliberate act (ordinarily I would say "conscious", but we are talking about LLMs here). Often, that which is removed is the nuance of near-synonyms (there is no pause to consider whether the author intended that nuance). I don't know if the "character" imparted by broader grammatical or structural choices can be called "semantic", but that also seems like a big part of what goes missing in the "LLM house style".

Bluntly: getting AI to "improve" writing, as a fully generic instruction, is naturally going to pull that writing towards how the AI writes by default. Because of course the AI's model of "writing quality" considers that style to be "the best"; that's why it uses it. (Even "consider" feels like anthropomorphizing too much; I feel like I'm hitting the limits of English expressiveness here.)

Because even though at work it looks like you’re tasked with creating use values, you’re only there as long as the use values you create can be exchanged in the market for a profit. So every humane drive to genuinely improve your work will clash with the external conditions of your existence within that setting. You’re not there to serve people, create beautiful things, solve problems, nu-uh. You’re there to keep capital flowing. It’s soulless.

I am really conflicted about this because yes, I think that an LLM can be an OK writing aid in utilitarian settings. It's probably not going to teach you to write better, but if the goal is just to communicate an idea, an LLM can usually help the average person express it more clearly.

But the critical point is that you need to stay in control. And a lot of people just delegate the entire process to an LLM: "here's a thought I had, write a blog post about it", "write a design doc for a system that does X", "write a book about how AI changed my life". And then they ship it and then outsource the process of making sense of the output and catching errors to others.

It also results in the creation of content that, frankly, shouldn't exist because it has no reason to exist. The number of online content that doesn't say anything at all has absolutely exploded in the past 2-3 years. Including a lot of LLM-generated think pieces about LLMs that grace the hallways of HN.

YES this hits the nail on something I've been trying to express for some time now. Semantic ablation: love it, going to use that a lot not now when arguing why someone's ChatGPT-washed email sucks.

Semantic ablation is also why I'm doubtful of everyone proclaiming that Opus 4 would be AGI if we just gave it the right agent harness and let all the agents run free on the web. In reality they would distill it to a meaningless homogeneous stew.

I think it is also fairly similar to the kind of discourse a manager in pretty much any domain will produce.

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.