Utilizing the API’s “logit_bias” to combat em dashes led me to suppress 106 tokens—here’s what I discovered and the code for your own “Dash-Free” output comparison (Variation 33)

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

If you’ve ever struggled with AI models inserting excessive em dashes (—) in responses, you’re not alone. Many users find that despite various instructions, models persistently generate these punctuation marks, often disrupting the flow of text. Fortunately, there’s a technique involving the OpenAI API’s logit_bias parameter that can significantly reduce or eliminate such behavior.

Understanding logit_bias

The logit_bias parameter offers a way to influence a model’s token preferences by assigning biases between -100 and 100. A bias of -100 effectively suppresses specific tokens, making it highly unlikely for the model to produce them.

The Challenge of Em Dashes and Variants

Initially, one might target the em dash (—) by identifying its token ID and setting its bias to -100. However, due to the way tokenization works—where symbols can combine with other characters to form new tokens—simply targeting one token isn’t sufficient. For example, if the model can switch to en dashes (–) or hyphens (-), it might still use these to emulate an em dash unless all related tokens are addressed.

An Effective Strategy: Going Beyond Basic Suppression

Through experimentation, it turns out that suppressing approximately 106 tokens related to dashes and their variants is necessary to achieve a clean ‘No dash’ response. Here’s what the process looked like:

1. Start by suppressing tokens that are simply the em dash (—), including surrounding spaces.
2. Expand to all tokens containing the em dash, such as attached words or characters.
3. Address similar dash variants like en dashes (–) and hyphens (-), applying similar suppression to tokens that involve these characters.
4. For hyphens that are outside of words (e.g., standalone hyphen), set their bias to -100 to prevent the model from using them as substitutes.

Results and Observations

Implementing this comprehensive token suppression had minimal adverse effects on the responses’ overall quality. In fact, models like GPT-4, when instructed to avoid dashes, responded more consistently without losing coherence or nuance.

Sample Evaluation

In a test involving two different model outputs—one normal and one with suppressed dash tokens—you’ll notice that the responses without em dashes maintain clarity and professionalism. This demonstrates that aggressive token biasing can effectively steer models away from unwanted punctuation without degrading