Exploring the “logit_bias” Parameter: How I Tackled Em Dashes and Had to Cancel 106 Tokens—Find My Insights and Code for a “No Dash” Response Test (Variation 42)

How to Eliminate Em Dashes from AI Responses Using Logit Biasing in OpenAI’s API

In the ongoing quest to refine AI-generated content, one common annoyance is the frequent and unwelcome appearance of em dashes (—). Despite various attempts—like custom instructions and memory settings—total suppression often remains elusive. An innovative approach involves leveraging the logit_bias parameter within the OpenAI API to significantly reduce or eradicate em dashes from outputs.

Understanding the Challenge

Em dashes are represented by specific tokens recognized by the language model. However, due to tokenization nuances—like when symbols combine with adjacent characters—simply disabling the token for the em dash may not suffice. The model can still generate variants such as en dashes (–), hyphens (-), or even combined forms that mimic em dashes. Overcoming this requires a comprehensive strategy.

The Logit Bias Technique

The logit_bias parameter allows for assigning biases to specific token IDs, scaling their likelihood of appearing in the output between -100 and 100. Setting a token’s bias to -100 effectively prevents it from being generated. To suppress all forms related to em dashes, one must identify all relevant token IDs—covering the em dash itself, its variants like en dashes, hyphens in various contexts, and their combinations—and assign them a -100 bias.

Implementation and Results

In practice, suppression may require biasing a surprisingly high number of tokens—often over 100—to fully eliminate the appearance of dashes. For example, setting biases for:

• The em dash token
• Tokens involving “—” with surrounding characters
• En dashes and their variants
• Hyphens not adjacent to alphabetic characters

…can cumulatively suppress these symbols across the model’s outputs.

After applying these biases, the responses tend to favor alternatives like commas, parentheses, or other punctuation that fulfill similar roles without using dashes. Interestingly, more sophisticated models (like GPT-4 variants) show a clearer preference for dash-free responses after biasing, whereas some models might still produce minor dash hints.

Practical Demonstration

Consider the following prompts and their responses:

Prompt: “Provide a hot take in a paragraph.”

• Normal Output (without biasing): Contains em dashes, stylistic choices
• Bias-Adjusted Output: The model shifts away from using dashes, opting instead for commas, parentheses,