Variation 12: How I employed the “logit_bias” parameter in the API to combat em dashes—and had to block 106 tokens! Insights and code for your own “Dash-Free” response test

Mastering Em Dashes in AI Responses: A Practical Approach with Logit Bias Optimization

For content creators and developers working with OpenAI’s API, controlling the style and orthography of AI-generated text can be quite challenging—especially when it involves delicate punctuation like em dashes. Frustrated by the persistent appearance of em dashes despite attempts with custom instructions and memory features, I explored a more tangible method: leveraging the logit_bias parameter to suppress these characters directly at the token level.

The Challenge of Em Dashes in Text Generation

During my experiments, I noticed that simply instructing the model to avoid em dashes wasn’t enough. The model would still produce them, often using various related tokens such as en dashes or hyphens, simply to keep the stylistic intention alive. To combat this, I identified the token IDs associated with these dash characters and methodically applied negative biases.

The Iterative Biasing Strategy

My approach involved incrementally adjusting the bias for tokens linked to dash characters:

• Initially targeting just the exact em dash token (—), I set its bias to -100.
• Gradually, I included tokens that presented variants like space-dash (— or —) and even those combining with letters.
• As the model adapted, it shifted toward using en dashes or hyphens, prompting further biasing of tokens associated with those forms.
• Ultimately, applying bias to a total of 106 tokens associated with em dashes, en dashes, and hyphens, I significantly reduced their occurrence without noticeably impairing the overall response quality.

Results and Implications

Testing with different models—ChatGPT’s latest iteration, as well as mini variants—showed that even after suppressing 106 tokens, the AI still produced coherent, contextually appropriate responses. Notably, models that tend to prefer more “saturated” language (like GPT-4 standard) leaned toward anti-dash responses, confirming that aggressive biasing can influence stylistic choices without sacrificing comprehension.

Practical Implementation

I’ve prepared a Python script that:

• Accepts a prompt via command line.
• Applies the predefined list of token biases.
• Executes the API request with these biases in place.

You’ll just need to:

1. Ensure your OPENAI_API_KEY environment variable is set.
2. Make the script executable.
3. Run it with your desired prompt.

Here’s [the full script