Using the “logit_bias” Parameter to Prevent Em Dashes in API Responses: My Experience with Suppressing 106 Tokens and How to Replicate the “No Dash” Approach

Transforming Em Dashes in AI Responses: How Biasing Tokens Can Eliminate Unwanted Punctuation

In the pursuit of cleaner, dash-free AI-generated text, many developers and content creators face the persistent challenge of em dashes slipping into responses despite various instructions. Recently, I experimented with the OpenAI API’s “logit_bias” parameter—a powerful tool that allows direct influence over the likelihood of specific tokens appearing in generated text.

My goal was straightforward: prevent AI models from using em dashes. Initially, I targeted the token ID representing the em dash (“—”) and set its bias to -100, hoping to suppress it entirely. However, AI models are clever: they often generate related tokens or combine symbols, meaning that simply biasing the em dash token wasn’t enough. For example, the model would switch to en-dashes or hyphens, often as part of different token combinations.

To address this, I systematically expanded the suppression beyond just “—” and targeted all tokens that could relate to dashes or hyphen-like characters. This included tokens containing “—” in various contexts, as well as hyphen tokens that could be used as substitutes in text. Ultimately, I found that biasing over 100 tokens (specifically 106) to -100 effectively minimized or entirely eliminated the appearance of em dashes in responses.

Here’s a summary of the process I followed:

• Initial attempt: Bias the em dash token with -100 — still saw some dash usage.
• Expanded scope: Bias tokens containing “—” and related symbols, increasing the number of tokens biased.
• Refined approach: Bias tokens representing hyphens and en-dashes, addressing the model’s tendency to substitute symbols.
• Final step: Bias over 106 tokens to -100, which significantly suppressed dash usage without adversely impacting response quality.

To illustrate the effectiveness, I compared responses to prompts like “Give me your hottest take” or “How would you solve US political division,” observing that models’ bias-affected responses shifted away from using em dashes, often favoring more neutral punctuation.

Interestingly, the more advanced models (e.g., gpt-4o-latest) appeared to prefer responses without dashes after this biasing approach, highlighting that large language models can be nudged to favor certain stylistic choices through token-level manipulation.

For those interested, I’ve compiled a Python script that demonstrates this biasing technique, along with the list of 106 tokens