Experimenting with the “logit_bias” Parameter in the API: How I Reduced Em Dashes and Managed 106 Suppressed Tokens — My Insights and Code for a “Dash-Free” Output Test

Harnessing the Power of logit_bias to Eliminate Em Dashes in AI Responses: A Deep Dive

For those working with the OpenAI API and seeking more control over generated content, suppressing unwanted tokens like em dashes can be quite challenging. Despite attempts using custom instructions and memory tweaks, em dashes often stubbornly persist in responses. However, leveraging the logit_bias parameter reveals a surprisingly effective workaround—one that involves suppressing a broad array of related tokens to achieve “No dash” outputs.

The Challenge with Em Dashes
Em dashes (—) are common in writing for emphasis or interruption, but they can be undesirable in specific contexts such as formal copy or technical documentation. Attempts to exclude them through traditional means often falter because the model adapts creatively, substituting hyphens, en dashes, or even combining characters that resemble em dashes. This behavior complicates content moderation and stylistic control for developers and writers relying on the API.

A Systematic Approach: Setting logit_bias to -100
The key insight is that tokens in the model become part of a complex tokenization system—certain characters and symbols don’t have a single dedicated token, and can form multiple variants. To counter this, the strategy involves identifying all tokens related to the em dash and its variants, then suppressing them via logit_bias.

Here’s how the process unfolded:

• Starting with initial suppression of tokens directly representing the em dash (—), but still seeing creative substitutions.
• Gradually expanding to include tokens associated with the em dash when attached to words or other symbols, recognizing that the model might generate hyphens or en dashes as substitutes.
• Increasing the suppression list to hundreds of tokens (up to 106 in total) that include hyphens, en dashes, or combined forms.

With this comprehensive suppression, the model reliably avoids producing em dashes and their substitutes.

Case Studies: Comparing Responses

Prompt: “In a paragraph, give me your best ‘hot take’”

• Standard response: Easily incorporates em dashes—long, dash-filled sentences.
• Suppressed response (logit_bias applied): Skillfully avoids em+dashes and hyphen variants, producing cleaner, dash-free output, often with slight stylistic differences but maintained coherence.

Similarly, in tackling complex prompts like opinions