How I Leveraged the “logit_bias” Parameter to Reduce Em Dashes—and Had to Cancel 106 Tokens! Insights and Code for Your Own “Dash-Free” Response Test

Title: Leveraging Logit Bias in ChatGPT API to Minimize Em Dash Usage: A Practical Approach

In the realm of AI prompt engineering, controlling the stylistic details of generated responses—such as the use of em dashes—can be surprisingly challenging. Recently, I embarked on an experiment to suppress em dash characters in GPT-4 outputs by utilizing the logit_bias parameter within the OpenAI API. This method involves assigning a bias value between -100 and 100 to specific token IDs, effectively discouraging the model from choosing certain tokens.

My primary goal was to prevent the model from incorporating em dashes, which appeared stubbornly persistent despite various conventional prompt modifications like custom instructions and memory adjustments. I began by identifying the token ID associated with the em dash (—), but soon realized that tokens representing this symbol could combine with surrounding characters to form new tokens—such as hyphens and en dashes—complicating the suppression effort.

To successfully diminish em dash usage, I found it necessary to bias a large set of tokens—up to 106 in total—containing all variants and contextual uses of these characters. Initially, setting biases on a small number of tokens led to minimal change. Gradually, expanding the biasing to a broader range, including tokens representing hyphens not flanked by letters, resulted in a significant reduction of em dash appearance.

Here’s a summary of the incremental process I followed:

• Biasing three core tokens representing the em dash itself.
• Extending to 40 tokens that involved any occurrence of the em dash with adjacent characters.
• Increasing to 62 tokens to cover en dashes and similar punctuation.
• Ultimately, suppressing 106 tokens, including hyphens and variants not touching letters on both sides.

Remarkably, after applying biases to all these tokens, the model’s tendency to produce em dashes was effectively eliminated in my tests—without severely compromising the quality of responses. For evaluation, I compared standard responses to prompts with and without biases, observing that the less “dash-happy” models favored responses that avoided em dashes altogether.

Maintaining this level of control does come with trade-offs; over-biasing could potentially affect overall response naturalness. However, this approach demonstrates that, even with a brute-force biasing strategy, significant stylistic control is achievable.

For practitioners interested in experimenting further, I’ve provided a Python script that applies this technique. It requires setting your `OPENAI_API