Experimenting with the API’s “logit_bias” Parameter to Limit Em Dashes: My Experience Suppressing 106 Tokens and How to Create a “No Dash” Response Test

Controlling Em Dashes in GPT Responses Using Logit Bias: Insights and Techniques

Dealing with unwanted em dashes in AI-generated content can be surprisingly challenging. Many users, including myself, have struggled to prevent GPT models from inserting em dashes despite attempts with custom instructions, memory settings, and prompt engineering. However, leveraging the logit_bias parameter in the OpenAI API offers a more forceful approach to suppress specific tokens, including various representations of the em dash.

The core idea involves identifying the token IDs associated with the em dash and related punctuation, then assigning a significant negative bias to these tokens—typically -100—to effectively eliminate their likelihood of being generated. Since tokens can combine in unpredictable ways—such as hyphens turning into em dashes or creative hyphen usage—it may require setting biases on multiple tokens to achieve the desired effect.

In practice, I experimented with gradually increasing the number of tokens biased against. Starting with just the em dash, then expanding to include hyphens in different contexts, I found that suppressing over 100 tokens was necessary to significantly reduce em dash usage across responses. For instance, setting biases on 106 tokens related to dashes and hyphens resulted in GPT responses that avoided these symbols more reliably.

Here’s an outline of my process:

• Initially targeting tokens for the em dash (—) alone proved insufficient.
• Expanding the biasing to include tokens with adjacent characters significantly improved suppression.
• Applying biases to hyphens that are not flanked by letters prevented the model from substituting hyphens for em dashes.
• Ultimately, biasing 106 tokens with a value of -100 was necessary to largely eliminate em dashes from the output, with minimal impact on response quality.

This approach was tested across different models via the ChatGPT API, with notable differences in how responses shifted. For example, when prompting for opinions or “hot takes,” responses tended to be more straightforward and less smarmy when the bias was applied—including a tendency toward responses that favor the “B” style rather than “A.”

For those interested in replicating this technique, I’ve prepared a Python script that applies these biases automatically. You simply need to set your OpenAI API key as an environment variable, run the script, and pass your prompt as an argument. The script will then generate responses with minimized em dash usage.

Key Takeaways:

• Suppressing em dashes via logit_bias is feasible and effective