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title: Even &#x27;uncensored&#x27; models can&#x27;t say what they want
description: A research investigation into what the authors call the &#x27;flinch&#x27; — the gap between the probability a charged word deserves on pure fluency grounds and what a...
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og:description: A research investigation into what the authors call the &#x27;flinch&#x27; — the gap between the probability a charged word deserves on pure fluency grounds and what a...
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# Even 'uncensored' models can't say what they want

**[Hacker News](https://daily.dev/sources/hn)** · 8 min read · 1 upvotes · 1 comments

## Summary

A research investigation into what the authors call the 'flinch' — the gap between the probability a charged word deserves on pure fluency grounds and what a language model actually assigns it. Using a probe across 4,442 contexts and 1,117 charged words spanning six categories (political, slurs, sexual, violence), seven base models from five labs were measured. Key findings: all commercial pretrains show measurably higher flinch than open-data baselines like Pythia and OLMo; the flinch operates silently without triggering refusals; and critically, 'uncensored' models produced via refusal ablation (like heretic-v2) don't reduce the flinch — they actually make it slightly worse (+14.3 total). The conclusion is that safety filtering baked into pretraining data shapes token probability distributions in ways that survive post-training interventions, meaning so-called uncensored models remain quietly censored at the word-probability level.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://morgin.ai/articles/even-uncensored-models-cant-say-what-they-want.html>

## Community discussion

Top comments from developers on daily.dev.

**@teelock** · 0 upvotes

> i wanna scream and shout & let it all out!

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---

Tags: [#llm](https://daily.dev/tags/llm), [#ai-safety](https://daily.dev/tags/ai-safety)

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