Experimenting with the API’s “logit_bias” to reduce em dashes led me to suppress 106 tokens—here are my results and the code for your own “Dash-Free” comparison

Overcoming Em Dashes in AI Responses: A Practical Guide for WordPress Developers

As AI models continue to influence many web projects, ensuring consistent, clean output remains a challenge—especially when it comes to punctuation quirks like em dashes. Frustrated by the persistent appearance of em dashes in responses, I explored a surprisingly effective method within the OpenAI API: leveraging the logit_bias parameter.

The Challenge of Em Dashes

Despite attempts with custom instructions and memory settings, the model often defaults to using em dashes, which can clutter content or be undesirable depending on style standards. Traditional approaches rarely control such fine-grained output nuances because tokens representing punctuation can combine with surrounding characters to create variants like en dashes or hyphens.

The Power of logit_bias

The logit_bias parameter allows us to assign specific biases to individual token IDs, ranging from -100 (for suppression) to +100 (for emphasis). The strategy involves identifying token IDs for unwanted characters—like — (em dash)—and setting their biases to -100 to discourage their use.

Step-by-Step Suppression Process

1. Identify Tokens: Use the tokenizer tools (like the tiktoken library) to find token IDs corresponding to —, en dashes, hyphens, and their variants.
2. Apply Biases Incrementally: Initially, set biases for the direct — tokens. When responses still include em dashes, expand biases to tokens containing — touching other characters.
3. Progressively Broaden Coverage: If necessary, include tokens representing en dashes, hyphens, and composite symbols. Adjust biases to -100 for these tokens.
4. Test Responses: Evaluate how the output changes when biases are applied. In my tests, suppressing approximately 106 tokens effectively eliminated em dashes without compromising response quality.

Practical Results

In tests with various models, heavily biasing tokens associated with dash characters resulted in responses that avoided em dashes altogether. Surprisingly, this approach did not significantly harm response coherence or tone.

For example:
– Without biasing: Responses often included em dashes.
– With targeted biases: Responses favored conventional punctuation, enhancing consistency.

Sample Code and Token List

I’ve compiled a Python script that automates token identification and bias application. You can find it [here](https://gist.github.com/kernkraft235/