Exploring the “logit_bias” Parameter: How I Reduced Em Dashes by Suppressing 106 Tokens and Developed a “No Dash” Response Test with Sample Code

Eliminating Em Dashes in AI-Generated Text: A Practical Approach Using Logit Bias

Struggling with unwanted em dashes in language generation? Many users have experienced the challenge of controlling specific character outputs in AI models like GPT-4. Recently, I explored a straightforward method to suppress em dashes and related dash characters by leveraging the logit_bias parameter in the OpenAI API.

The Problem

Despite efforts with custom instructions and memory settings, AI models often default to using em dashes (“—”), especially in nuanced or stylistic writing. This consistent behavior presents a dilemma when aiming for cleaner or more standardized punctuation.

The Solution: Targeted Token Suppression

The key insight lies in understanding how AI tokenization works. Tokens such as em dashes, en dashes, and hyphens are recognized as distinct tokens, but they can also combine with other characters, complicating suppression efforts.

Here’s what I discovered: by identifying the token IDs corresponding to these dash characters and applying a strong negative bias, you can significantly diminish their appearance. I set the logit_bias to -100 for over 100 tokens that include or relate to em and en dashes, effectively “dampening” their likelihood of being generated.

Progress Overview

• Initially, only the exact em dash token was targeted.
• As the model continued to produce em dash variants, I expanded the bias to include tokens with surrounding characters, such as spaces or letters touching the dash.
• When the model began substituting hyphens for em dashes, I further adjusted biases to hyphen tokens that aren’t adjacent to letters, setting those to -100 as well.

Results

Interestingly, even with such a comprehensive suppression—covering over 100 tokens—the model maintained coherent and accurate responses. In multiple tests, responses without em dashes or hyphens were achieved with minimal impact on overall quality. Less “dash-happy” models tended to favor the suppression method more strongly.

Practical Implementation

For those interested in replicating this technique, I’ve prepared a Python script that applies this bias automatically. The script requires:

• Your OpenAI API key as an environment variable.
• The list of token IDs related to dash characters, which you can find linked below.

Once set up, you can pass any prompt as a command-line argument, and the script will generate responses with suppressed dash characters, enabling cleaner, dash-free text generation.

Learn More & Access the Script

For developers