Exploring the ‘logit_bias’ Parameter in the API: How I Tackled Em Dashes and Had to Suppress 106 Tokens — Findings and Sample Code for a ‘No Dash’ Response Test

Harnessing the logit_bias Parameter to Minimize Em Dash Usage in AI Responses: A Practical Exploration

As developers and enthusiasts working with OpenAI’s API, many of us strive to refine the model’s output to match specific stylistic or structural preferences. One common challenge is preventing the AI from using em dashes (—), which often appear unexpectedly despite various prompt adjustments. In an effort to address this, I experimented with the logit_bias parameter—a powerful tool that allows us to influence token probabilities during generation.

Understanding the Challenge

Despite multiple attempts—such as customizing instructions and leveraging memory—the model stubbornly retained its penchant for em dashes. I realized that simply blocking the token for the em dash (its specific token ID) wasn’t sufficient because the model can generate variations. For example, it might substitute en dashes, hyphens, or even combine characters to produce similar visual effects.

The Solution: Intensive Token Suppression

By inspecting the tokenization process, I identified several token IDs associated with em dashes, en dashes, hyphens, and their adjacent character sequences. To effectively suppress these, I set their logit_bias values to -100, effectively disfavoring their selection during generation. Here’s an overview of the process:

• Initial suppression: Targeted tokens including the raw em dash (—).
• Expanded scope: Tokens that involve the em dash combined with surrounding characters.
• Further broadening: En dash (–), hyphen (-), and ambiguous tokens that could produce dash-like appearances.

Through iterative adjustments—culminating in suppressing 106 tokens—I significantly reduced em dash appearance in model responses.

Empirical Results

Below are summarized outcomes from different model configurations, highlighting how this approach influences the response style:

• In some models, responses that normally contain em dashes switched to more traditional punctuation or inline alternatives.
• Notably, models like GPT-4 (with or without custom instructions) showed a marked decrease in em dash usage after suppression, often replacing them with commas, parentheses, or plain hyphens.

Sample Response Comparison

Prompt: Provide your perspective on the obsession with productivity culture.

• Standard Response: Frequently contains em dashes to emphasize points.
• Post-Adjustment Response: Relies more on commas, parentheses, or colons, effectively removing em dashes from the output.

Prompt: *Describe how to address political