AI Writing Still Sounds Like AI Even When Model Bans Common Words

The developer's experiment reveals why current approaches to scrubbing AI-sounding prose face fundamental limits. Token-level suppression via logit_bias is mathematically enforced—a -100 penalty can reliably ban a word—but the model has no way to know when the blacklisted token was the right choice. A word like "appears" might hedge an uncertain claim that should be preserved, or it might be pure filler; the API parameter cannot tell the difference. Similarly, many words tokenize in multiple forms (" crucial" versus " Crucial"), and penalizing one fragment can accidentally suppress different words. The alternative, embedding the blacklist in the prompt, gives the model discretion to ignore it when higher priorities conflict, but that flexibility comes with a cost: the model may preserve a watched word anyway, or follow the list so rigidly that it introduces awkward synonyms or damages the underlying meaning.

The test results suggest there is no clean solution. OpenAI's removal of logit_bias from current models hints that the feature may have been deemed less useful than the engineering effort required to maintain it. OpenRouter's broad support across open and hosted models offers a playground for further experimentation, but even there, provider routing rules and parameter-handling inconsistencies mean that support is not guaranteed unless the user tests each endpoint separately. The developer's findings imply that making AI writing sound human likely requires not suppressing specific words at the API level, but instead retraining or fine-tuning models to learn a different voice—a much harder problem than token blacklisting can solve.