Exploring the “logit_bias” Parameter in the API: How I Confronted Em Dashes and Had to Suppress 106 Tokens—My Results and Code for a “No Dash” Response Test

Title: How to Minimize Em Dashes in ChatGPT Responses Using Logit Bias: A Practical Guide

If you’ve ever grappled with ChatGPT’s frequent use of em dashes and wished for a way to suppress them, you’re not alone. Despite trying custom instructions or memory settings, sometimes the AI just won’t stop adding those distinctive punctuation marks. Fortunately, there’s a powerful parameter in the OpenAI API—logit_bias—that can help you steer the output away from em dashes effectively.

Understanding the Challenge

ChatGPT’s penchant for inserting em dashes (—) can be persistent, even after multiple attempts to influence its style through instructions. The root of the issue lies in tokenization: each symbol or word is represented by tokens, but certain characters—like the em dash—may have multiple token representations or can combine with others to form different tokens, making suppression tricky.

The Strategy: Using logit_bias

logit_bias allows you to assign biases to specific tokens, nudging the model to prefer or avoid particular tokens during generation. Bias values range from -100 (strongly discourage) to 100 (strongly encourage). To suppress em dashes, one must identify all relevant tokens associated with the dash and set their bias to -100.

Implementation Journey

Here’s a summary of how I achieved a near-complete suppression:

1. Identify Em Dash Tokens:
   Initially, I targeted the straightforward token for the em dash, but it wasn’t enough. Since tokens can combine or adapt, I expanded the list to include variations like en dashes, hyphens, and tokens where these characters touch other symbols or letters.

2. Gradual Biasing:
   Starting with just three tokens (‘—’, ‘ —’, ‘— ‘), I increased the scope progressively:

3. Up to 40 tokens, covering all that include the em dash.
4. Around 62 tokens, I had to account for en dashes and their variants.

5. Finally, at approximately 106 tokens, I included hyphenated variations where hyphens could mimic em dash behavior.

6. Results:
   After biases were set to -100 for all these tokens, ChatGPT’s responses significantly reduced the use of em dashes. In my tests with different models, responses aligned with the “anti-dash” style, often better disliked than the default responses.

Sample Results

Prompt: “In