Utilizing the API’s “logit_bias” Parameter to Combat Em Dashes: How I Had to Suppress 106 Tokens and My Results for a “Dash-Free” Response Test

Enhancing Text Output by Suppressing Em Dashes in GPT-4 Responses: A Practical Approach Using logit_bias

In the quest for cleaner, more consistent text generation, many users face the challenge of unwanted em dashes (—) appearing in AI outputs. Whether for stylistic consistency, readability, or branding, controlling these punctuation marks can be surprisingly complex. Recent explorations reveal a straightforward yet effective solution through the OpenAI API’s logit_bias parameter, which allows fine-tuning of token probabilities to influence generated text.

The Challenge with Em Dashes

Despite attempts using custom instructions, memory, and prompt engineering, eliminating em dashes entirely proved difficult. The AI would often creatively circumvent restrictions by replacing em dashes with en dashes, hyphens, or alternative constructs. Recognizing that tokens for symbols and words can recombine or produce similar characters, a more aggressive technique was needed.

The logit_bias Solution

The logit_bias parameter assigns a bias score between -100 and 100 to specific token IDs, effectively discouraging or encouraging their use. The key to suppressing em dashes was identifying and heavily penalizing all tokens related to them:

• Tokens explicitly representing — (em dash)
• Tokens for en dashes and hyphens
• Tokens that incorporate these symbols with surrounding characters

By iteratively setting token biases to -100 (strongly discouraging their use), we gradually suppressed the AI’s reliance on these characters. In testing, it required blocking around 106 tokens related to dash representations:

• Initial sets: tokens containing —
• Expanded sets: tokens including any adjacent letters or punctuation that could produce similar dash characters
• Final set: hyphen tokens not flanked by letters, which, if left unchecked, could generate em-like dashes

This comprehensive biasing yielded responses devoid of em dashes, with minimal impact on semantic coherence.

Practical Implementation

Below is a summarized outline of the process:

1. Identify Tokens: Use tokenization tools or API exploration to find token IDs for —, en dashes, hyphens, and related variations.
2. Apply Biases: Construct a dictionary setting each identified token ID to -100.
3. Generate Text: Pass this bias configuration via the logit_bias parameter in your API call.
4. Evaluate Results: Compare responses with and without biases to ensure style consistency.

Sample Evaluation Results

Using