Using the “logit_bias” Parameter in the API to Fight Em Dashes: My Experience Suppressing 106 Tokens and a Code Comparison for a “Dash-Free” Response

Harnessing logit_bias to Eradicate Em Dashes in AI Text Generation: Insights and Practical Implementation

Dealing with persistent em dashes in AI-generated text can be a frustrating challenge, especially when traditional methods like custom instructions and memory adjustments fall short. Recently, I explored an alternative approach using the OpenAI API’s logit_bias parameter—a powerful feature that allows you to influence token probabilities during response generation. My goal was to suppress em dashes and related hyphen-like tokens effectively.

The Challenge with Em Dashes

Despite setting specific instructions, the model often continues to insert em dashes, which can interfere with the desired tone or formatting. Recognizing that tokens representing symbols like em dashes might also combine with other characters, I decided to take a more aggressive route: bias the model against producing these tokens altogether.

The Approach: Setting Biases on Tokens

Using the logit_bias parameter, I assigned a bias of -100 (the minimum value) to tokens corresponding to em dashes, en dashes, hyphens, and any composite tokens that include these. However, because tokens are not always straightforward—some symbols can form multiple tokens depending on context—I discovered I had to identify and bias a total of 106 tokens to fully suppress the dash-like behavior.

Progression and Findings

• Biasing 3 tokens directly related to the em dash: negligible impact.
• Extending to 40 tokens including any variants with the em dash: partial success.
• Increasing biasing to 62 tokens, covering en dashes and similar symbols: improved suppression.
• Ultimately, applying bias to all 106 tokens, including hyphens used as em dashes, resulted in near-complete elimination of the unwanted characters.

This blunt-force strategy, while seemingly heavy-handed, did not noticeably degrade the overall quality or coherence of responses, especially in models like GPT-4 with the latest training data.

Practical Evaluation

I tested responses to prompts requesting “hot takes” and on topics like political balkanization. Comparing normal responses with those generated after applying the logit bias, I observed a consistent reduction—or complete absence—of em dashes and hyphens in the biased outputs. Interestingly, models known to be more sensitive to stylistic cues (like GPT-4) tended to favor responses aligned with the biasing, supporting the effectiveness of this approach.

Implementation Resources

For practitioners interested in replicating this method, I