Experimenting with the “logit_bias” parameter to eliminate em dashes: how I suppressed 106 tokens and what I learned, plus a code comparison for creating a “No dash” reply

Mastering Em Dashes in AI Text Generation: A Practical Approach Using Logit Bias on the OpenAI API

Dealing with Em Dashes in AI-Generated Content

If you’ve ever interacted with AI language models like GPT-4, you might have noticed how stubborn em dashes (—) can be. Despite various prompts and instructions, these punctuation marks often persist, impacting the readability and style of generated text. As a content creator or developer aiming for cleaner output, you may seek effective techniques to minimize or eliminate em dashes from responses.

Leveraging the logit_bias Parameter for Punctuation Control

One promising method involves the use of OpenAI’s logit_bias parameter. This feature allows you to influence the likelihood of specific tokens appearing in the output by assigning bias values ranging from -100 (strongly discouraged) to 100 (strongly encouraged). The strategy is to identify the token IDs associated with undesired characters—like the em dash—and set their bias to -100 to suppress their appearance.

The Challenge of Token Variations

It’s important to recognize that symbols such as the em dash can be represented by multiple tokens, especially since the model may generate different forms like en dashes (–) or hyphens (-) when attempting to mimic em dashes. To effectively diminish their presence, one must target all relevant tokens and their variants, including those that combine with other characters or are used in different contexts.

An Iterative Approach to Suppression

In practice, a single adjustment often isn’t enough. Here’s an outline of an iterative methodology:

1. Initial Targeting: Start by identifying tokens that directly correspond to the em dash.
2. Expand Scope: Include tokens that contain or are related to em dashes, such as those with surrounding whitespace or adjacent letters.
3. Broaden to Similar Signs: Monitor and include en dashes and hyphens, especially those that might be substituted or used in similar contexts.
4. Aggressive Suppression: If the em dash still appears, progressively add more related tokens—sometimes requiring hundreds of tokens to be suppressed simultaneously.

In a recent experiment, setting 106 different tokens related to dashes and hyphens to a bias of -100 effectively eliminated em dashes from generated responses without significantly degrading the quality of the content.

Practical Implementation and Results

Here’s a snapshot of the process:

• Initially, only 3 tokens related directly to the em dash were suppressed.
• After