Leveraging the “logit_bias” Parameter in the API to Minimize Em Dashes: My Experience with Suppressing 106 Tokens and a Code Guide for “Dash-Free” Responses

How to Eliminate Em Dashes in AI-Generated Text Using OpenAI’s API: A Practical Guide

If you’ve ever struggled with ChatGPT or GPT-4 inserting unwanted em dashes (—) into its responses, you’re not alone. Many users seek more control over the generated content’s style, especially avoiding characters like em dashes that can disrupt formatting or readability. Recently, I experimented with a straightforward yet effective method: leveraging the logit_bias parameter in OpenAI’s API to suppress em dash tokens and their variants, achieving remarkably clean outputs.

Background: Tackling Unwanted Em Dashes in AI Responses

Despite various prompt-engineering techniques—like custom instructions or system messages—completely preventing em dashes proved challenging. I noticed that even when trying to exclude the em dash directly, the model often resorted to hyphens or other dash-like characters, especially in more nuanced contexts. Recognizing that these symbols are represented by specific token IDs within the model’s vocabulary, I turned to the logit_bias parameter, which allows us to assign bias values between -100 and 100 to specific tokens, effectively encouraging or discouraging their usage.

Methodology: Sequential Suppression of Dash Variants

Initially, I targeted the primary em dash token. However, since the tokenizer can combine characters into composite tokens—like en dashes, hyphens, or hybrid characters—I progressively expanded the suppression. Here’s the overall process:

1. Identify Token IDs: Determine the token IDs corresponding to the em dash (—), en dash (–), hyphen (-), and similar symbols.
2. Set Biases in Steps:
3. Step 1: Bias the tokens directly matching the em dash.
4. Step 2: Extend suppression to tokens involving the em dash surrounded by characters or in combination with other symbols.
5. Step 3: Broaden the scope to include hyphens and en dashes, applying -100 bias to all tokens representing variations not desired.
6. Achieve Stroke-Ready Clean Responses: After setting biases on over 100 tokens, the model’s inclination to use these characters diminishes significantly.

Results and Observations

Here’s a summary of the evolution:

• Initially: The model comfortably uses em dashes, even when prompted otherwise.
• After biases for 40 tokens: It begins replacing em dashes with hyphens or alternative punctuation, but sometimes reverts