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.
I believe that good communication has four attributes.
- It’s approachable: it demands from the reader (or hearer, or viewer) the least amount of reasoning and previous knowledge, in order to receive the message.
- It’s succinct: it demands from the reader the least amount of time.
- It’s accurate: it neither states nor implies (for a reasonable = non-assumptive receiver) anything false.
- It’s complete: it provides all relevant information concerning what’s being communicated.
However no communication is perfect and those four attributes are in odds with each other: if you try to optimise your message for one or more of them, the others are bound to suffer.
Why this matters here: it shows the problem of ablation is unsolvable. Even if generative models were perfectly competent at rephrasing text (they aren’t), simply by asking them to make the text more approachable, you’re bound to lose info or accuracy. Specially in the current internet, where you got a bunch of skibidi readers who’ll screech “WAAAAH!!! TL;DR!!!” at anything with more than two sentences.
I’d also argue “semantic ablation” is actually way, way better as a concept than “hallucination”. The later is not quite “additive error”; it’s a misleading metaphor for output that is generated by the model the same way as the rest, but it happens to be incorrect when interpreted by human beings.
This is a good name for one of the main reasons I’ve never really felt a desire to have an LLM rephrase/correct/review something I’ve already written. It’s the reason I’ve never used Grammarly, and turned off those infuriating “phrasing” suggestions in Microsoft Word that serve only to turn a perfectly legible sentence into the verbal equivalent of Corporate Memphis.
I’m not a writer, but lately I often deliberately edit myself less than usual, to stay as far as possible from the semantic “valley floor” along which LLM text tends to flow. It probably makes me sound a bit unhinged at times, but hey at least it’s slightly interesting to read.
I do wish the article made it clear if this is an existing term (or even phenomenon) among academics, something the author is coining as of this article, or somewhere in between.
GPT-4o mini, “Rephrase the below text in a neutral tone”:
This name is appropriate for one key reason: I have not felt the need to use an LLM for rephrasing, correcting, or reviewing my writing. This is also why I have not utilized Grammarly and have disabled the “phrasing” suggestions in Microsoft Word, which often transform a clear sentence into something overly corporate or generic.
Although I wouldn’t categorize myself as a writer, I have been intentionally editing myself less than usual lately to avoid the typical style associated with LLM-generated text. This approach might come across as unconventional at times, but it can also make for more engaging reading.
I also wish the article clarified whether this term is already established in academic circles, if the author is introducing it for the first time, or if it falls somewhere in between.
“avoid the typical style associated with LLM-generated text” – slop!
That’s a fine illustration of the problem, whatever it’s properly called.
Having paused to search the web I find that “ablation” according to wikipedia is a term used in AI since 1974. Arxiv.org has a recent paper talking specifically about “semantic ablation” which phrase it uses to describe an operation deliberately removing semantic information from an LLM’s representation of a sentence in an attempt to see what purely syntactical information is left over afterwards, or something like that.
I’m not sure if that writer gets all the details right when it comes to how it works, but I do like “semantic ablation.” It’s good to finally have a name for that after we’ve already seen so much of it.
It’s statistical blandness writ large.
The stack of single-sentence paragraphs after the introduction paragraph trying so hard to have an impact.
The tendency to put “not X, not Y, just Z” everywhere.
The perfect conclusion written at the end of each piece , summarising three bland paragraphs with yet another bland paragraph.
Statistically regurgitated bullshit, all of it
A stack of single-sentence paragraphs, you say?
With a perfect conclusion written at the end you say?
Methinks I’ve seen this before somewhere, I say.
Dare to be different, I say.
Could have just said popularity breeds mediocrity and it works on that level, but I appreciate this term too.
I recently read a lovely short story about this: https://sightlessscribbles.com/the-colonization-of-confidence/
