Leveraging the “logit_bias” Parameter in the API to Combat Em Dashes: My Experience Suppressing 106 Tokens and a Guide to “No Dash” Response Testing

Eliminating Em Dashes in AI Responses: A Deep Dive into Logit Bias Optimization

In the evolving landscape of AI prompt engineering, enthusiasts and developers often seek ways to fine-tune the model’s stylistic tendencies. One such challenge is controlling the use of em dashes (—), which many find distracting or stylistically inconsistent for their applications. Recently, I experimented with the OpenAI API’s logit_bias parameter to suppress em dashes effectively and documented an intriguing process worth sharing for those interested in nuanced model calibration.

Tackling the em dash dilemma with standard techniques—like custom instructions or memory adjustments—proved futile. The model persisted in deploying em dashes regardless. Recalling that logit_bias allows direct nudging of token probabilities (from -100 to 100), I decided to target tokens associated with em dash characters and their variants. Initially, I isolated the token ID for the em dash (—) but quickly realized that the model can generate related tokens such as en dashes, hyphens, and even combined symbols through different tokenizations.

To genuinely steer the model away from producing any form of dash, I incrementally expanded the set of biased tokens. First, I suppressed all tokens containing or touching the em dash, then extended biasing to en dashes and hyphens when they began substituting for em dashes in responses. It took setting 106 tokens to -100 to effectively eliminate em dash usage across multiple testing scenarios.

Here’s an overview of the escalation:
– With just 3 tokens related directly to em dashes, the model stubbornly continued their use.
– Increasing bias to 40 tokens that include any appearance of “—” began to diminish their frequency.
– Further extending bias to 62 tokens, the model shifted to en dashes as replacements.
– Finally, setting 106 tokens — covering hyphens not flanking letters and other variants — effectively suppressed all forms of dashes, including those hyphens adopting em dash functions.

This extensive token biasing did not appear to significantly impair the model’s ability to generate coherent, relevant responses. Interestingly, the models showed a tendency to favor responses that naturally avoided dashes when biased sufficiently.

To validate this approach, I conducted manual evaluations by feeding various prompts to different GPT models (GPT-4, GPT-4.1, GPT-4.5, GPT-3, and mini models) with all custom instructions and memories disabled.