Exploring the “logit_bias” API parameter to eliminate em dashes: My experience suppressing 106 tokens and a comparison of “No dash̶ responses with sample code

How to Minimize Em Dashes in AI Responses Using OpenAI’s Logit Bias Parameter

Are you tired of seeing unwanted em dashes in AI-generated text? If so, you’re not alone. Many developers and content creators struggle to control punctuation styles, especially with complex symbols like em dashes. Luckily, OpenAI provides a powerful yet underutilized tool called the logit_bias parameter that can help shape AI output more precisely.

Understanding the Challenge

Em dashes (—) are versatile punctuation marks often used for emphasis or interruption. However, AI models frequently insert them unexpectedly, sometimes compromising the tone or readability of generated content. Traditional approaches—like instructing the model through custom prompts or limiting memory contexts—often fall short because the model tends to favor its learned punctuation habits.

The Power of Logit Bias

The logit_bias parameter allows you to influence the likelihood of specific tokens appearing in the output by assigning a bias value between -100 and 100. Setting a token’s bias to -100 effectively suppresses its chances of being generated. The challenge lies in accurately identifying all token variations that could represent an em dash, especially when the model compensates by using similar symbols like hyphens or en dashes.

An Iterative Suppression Strategy

To effectively suppress em dash occurrences, you need to:

1. Identify all token IDs associated with the em dash and related symbols.
2. Apply a bias of -100 to each token ID to prevent them from appearing.
3. Recognize that the model may substitute similar characters like hyphens or en dashes, requiring suppression of those as well.

Through experimentation, setting biases on over 100 tokens—covering all variations and surrounding contexts—proved successful in minimizing em dash appearances without significantly impacting the quality or coherence of responses.

Practical Implementation

Here’s a summarized overview of the approach:

• Start with targeting the direct em dash token.
• Expand bias application to tokens containing “—” with neighboring characters.
• Include en dashes (–) and hyphens (-) when they are not part of words.
• Increase the count of suppressed tokens gradually until the behavior diminishes, as demonstrated in tests where 106 tokens were suppressed.

Sample Results

In testing with different models—like GPT-4, GPT-4 with custom biases, and smaller variants—the suppression strategy consistently shifted responses from containing em dashes (“—”) to alternatives like commas or parentheses, or eliminated the dashes altogether.