opinion: The subtractive bias we're ignoring

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.

  • Lvxferre [he/him]@mander.xyz
    1·
    1 hour ago

    I believe that good communication has four attributes.

    1. 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.
    2. It’s succinct: it demands from the reader the least amount of time.
    3. It’s accurate: it neither states nor implies (for a reasonable = non-assumptive receiver) anything false.
    4. 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.