cross-posted from: https://ibbit.at/post/178862
spoiler
Just as the community adopted the term "hallucination" to describe additive errors, we must now codify its far more insidious counterpart: semantic ablation.
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).
During "refinement," the model gravitates toward the center of the Gaussian distribution, discarding "tail" data – the rare, precise, and complex tokens – to maximize statistical probability. Developers have exacerbated this through aggressive "safety" and "helpfulness" tuning, which deliberately penalizes unconventional linguistic friction. It is a silent, unauthorized amputation of intent, where the pursuit of low-perplexity output results in the total destruction of unique signal.
When an author uses AI for "polishing" a draft, they are not seeing improvement; they are witnessing semantic ablation. The AI identifies high-entropy clusters – the precise points where unique insights and "blood" reside – and systematically replaces them with the most probable, generic token sequences. What began as a jagged, precise Romanesque structure of stone is eroded into a polished, Baroque plastic shell: it looks "clean" to the casual eye, but its structural integrity – its "ciccia" – has been ablated to favor a hollow, frictionless aesthetic.
We can measure semantic ablation through entropy decay. By running a text through successive AI "refinement" loops, the vocabulary diversity (type-token ratio) collapses. The process performs a systematic lobotomy across three distinct stages:
Stage 1: Metaphoric cleansing. The AI identifies unconventional metaphors or visceral imagery as "noise" because they deviate from the training set's mean. It replaces them with dead, safe clichés, stripping the text of its emotional and sensory "friction."
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.
Stage 3: Structural collapse. The logical flow – originally built on complex, non-linear reasoning – is forced into a predictable, low-perplexity template. Subtext and nuance are ablated to ensure the output satisfies a "standardized" readability score, leaving behind a syntactically perfect but intellectually void shell.
The result is a "JPEG of thought" – visually coherent but stripped of its original data density through semantic ablation.
If "hallucination" describes the AI seeing what isn't there, semantic ablation describes the AI destroying what is. We are witnessing a civilizational "race to the middle," where the complexity of human thought is sacrificed on the altar of algorithmic smoothness. By accepting these ablated outputs, we are not just simplifying communication; we are building a world on a hollowed-out syntax that has suffered semantic ablation. If we don't start naming the rot, we will soon forget what substance even looks like.
Can someone translate this? I get that AI tends to be a bit too low-common-denominator, but this reads like a scientific journal on a subject I've never studied
So text generation ai works as a word prediction algorithm, finding whatever word is most likely to come next. When used to edit work , this along with the way models are tuned will naturally choose more likely and therefore more simple words over more complicated words that convey more nuance and meaning , simplifying and dumbing down our writing.
Instead of using more specific words and information it pares things down and simplifies them in ways that destroy nuanced meaning that was the point of using those specific words and information in the first place, this is bad because it's dumbing down output that is already dumbing down the people reliant on using it
Semantic: Having to do with words, or word choice in a particular text. (EDIT: also, crucially, meaning within a text)
Ablation: The erosion or stripping awsy of the surface layer of a material under applied force, especially high-speed winds.
Algorithmic: Having to do with the use of an algorithm (an equation that specifies a particular output for a particular input).
High-entropy: A bit complicated to explain, but essentially means 'complicated' or 'dense' in this context. 'High-entropy information' is referring to information that communicates a lot of data with a small amount of communication. Consider a terse telegram vs a children's book.
"Semantic ablation is the algorithmic erosion of high-entropy information" therefore refers to the automatic 'stripping away' of complex language in favor of simplified language by LLMs.
Gaussian distribution: A distribution of probabilities that peak in the middle of the range. A Gaussian distribution will favor 'average' results quite strongly. Yes, it's more complicated than that, but that all you need for this article. The paragraph containing this discusses why LLMs are dumbing down language: they remove rare, precise terminology in favor of mundane words.
Romanesque, Baroque, ciccia: It's describing a masterful art (carvings from Roman masters) being superficially copied by cheap knock-offs.
Entropy decay: Loss of information density/complexity.
Lexical: Relating to a vocabulary or set of words in a language or text.
That should be most of the unusual words. You should be able to get the gist of the article from that. Lemme know if there's anything else you're struggling with.
Honestly, they've made a good case for why some ablation is necessary ⸻ This article cpuld have been just as succinct but much more comprehensible to a reader by simply using words people encounter outside of psychology textbooks.
The reason AI does it is because it picked up good practice from some of its sources, which is to write to the audience.
The author makes a good point that words like "ablation" can spice up writing and should be sprinkled throughout texts here and there, but this article way overdoes it.
Like if AI is using the center of the bellcurve to the detriment of the edge, this author seems to be selecting entirely from the extremes
I'm afraid I have to disagree entirely. Nothing in the article was too far out of the bounds of what I would consider normal. Sure, some of the technical language (entropy, Gaussian) might not be encountered unless you're into nerdy stuff, but that's an easy dictionary search away. The rest can be inferred from context (I desperately hope you have been taught how to do this?).
Example: the relationship between Romanesque and Baroque art. Never heard of it before. I inferred it from a vague knowledge of history and the rest of the paragraph. It's not magic; anyone can do it, including you.
I'm practically begging you here: if the above article was difficult for you, reread it as many times as you need in order to understand it. Then go seek out more works that are difficult to understand and conquer those too. If we lose the ability to do this, we're in for a long slide into Hell.
Im familiar with entropy, Gaussian, and have at least heard of ablative armor, but the concept of an AI being somehow like ablative armor was a concept that didnt click for me. When I went in looking for clarification, I encountered more and more of this. High-entropy language? How does this relate to things going from states of higher energy potential to lower energy potential?
These are rhetorical questions at this point, so there's no need to answer them, I just found this unnecessarily dense.
I assure you, I'm plenty literate and have digested works like these before. It's just a massive pain in the assfor an article that doesn't require this level of obfuscation to communicate the point. Prose should be exactly as complicated as it needs to be, and no more. There are much more literate people than I that have said the same thing. This was pure pretension masquerading as wit.
The example posted here with this article being run through AI is great btw. Shows how "simple" tokens win out in the end.
Remember, simplification of concepts is fine, but those concepts need to exist in their original state to be expanded upon. When these flattened states begin to take over it ends up just flattening everything. It's the "average man" contradiction but on the scale of the printing press.
I feel like the author's concepts are simple enough, and I agree with them. It's the language that's needlessly hifalutin.
Yeah. But the reason that language is chosen is specifically so when you run it through a LLM you get a significant flattening. Or at least that was my take away.
The summaries also miss some points in that process since the original article is so dense.
Ask ai to do it
no