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.
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.
Ha, If you’re alluding to my post being similar to generated output, you obviously haven’t experienced the pure blandness of LLMs trying to write engaging content.