Utilizing the “logit_bias” Parameter in the API to Combat Em Dashes: My Experience with Suppressing 106 Tokens and How to Create a “No Dash” Output Comparison (Variation 37)

Mastering the Art of Eliminating Em Dashes in AI Responses Using the Logit Bias Parameter in OpenAI’s API

Dealing with unwanted em dashes in AI-generated text can be a persistent challenge, especially when fine-tuning prompts and instructions fall short. Recently, I experimented with leveraging the “logit_bias” parameter in the OpenAI API to suppress em dashes and related hyphenated tokens. After extensive testing, I discovered that setting biases for over 100 individual tokens was necessary to effectively eliminate their occurrence without degrading overall response quality.

The process involved identifying various token IDs representing the em dash, en dash, hyphen, and their combinations. Since tokenization sometimes produces composite tokens, simply biasing the primary symbol was insufficient. I incrementally increased the bias, starting with fewer tokens and gradually expanding to cover all variants—ultimately applying a bias of -100 to 106 tokens associated with dashed and hyphenated characters.

In practical tests, responses from models like GPT-4 and its derivatives showed significant reduction in em dash usage. For example, when requesting a “hot take” or a proposed solution to political polarization, the biased models favored responses that avoided em dashes entirely, aligning more with traditional punctuation styles.

Here are some key observations:

• Using the bias on just a few tokens minimally impacted output.
• Boosting the bias to cover broader token sets led to a clean elimination of em dashes.
• Even “smarter” models tend to prefer responses with fewer hyphenated symbols when biased correctly.
• While ideal solutions involve model fine-tuning or training adjustments, a brute-force approach with a large bias set offers a practical interim method.

For those interested in experimenting further, I’ve compiled the list of 106 biased tokens and a Python script to automate the process. The script requires setting your OPENAI_API_KEY environment variable. Simply make it executable, pass your desired prompt as a command-line argument, and observe the dash-free responses.

Check out the full details, including the token list and script, here: GitHub Gist

This approach showcases how strategic token biasing can significantly refine AI output, especially for specific stylistic or formatting preferences without extensive retraining.