Leveraging the “logit_bias” Setting in the API to Tackle Em Dashes: My Experience Suppressing 106 Tokens and a Code Guide for Creating a “Dash-Free” Output Comparison

Overcoming Em Dashes in AI Responses: A Deep Dive into Logit Bias Tweaks for Cleaner Text Generation

In the quest for more polished and consistent AI-generated content, one persistent challenge has been controlling the appearance of em dashes—those long, punctuation marks often used for emphasis or interruption. Despite various attempts with custom instructions and memory settings, the AI would stubbornly insert em dashes, complicating clean text generation.

An innovative solution emerged through the use of OpenAI’s logit_bias parameter. This feature allows developers to assign biases—ranging from -100 to 100—to specific token IDs, effectively encouraging or discouraging their inclusion in the output. Initially, the goal was straightforward: identify the token ID for the em dash (—) and set it to a strong negative bias (-100) to prevent its appearance. However, it quickly became clear that the model tended to combine tokens in unpredictable ways, creating surrogate characters like hyphens or en dashes to bypass the bias.

To address this, a systematic approach was adopted. The process involved first targeting tokens that directly represented the em dash, then expanding to include any token sequences involving the dash, enclosed punctuation, or similar characters. The most comprehensive step involved assigning a negative bias to 106 different tokens associated with hyphenated variants and similar symbols. While seemingly drastic, this method effectively suppressed the em dash and its variants without noticeably degrading the overall response quality.

Below is an overview of the progression and methodology:

• Initial Phase: Targeted individual tokens such as '—', ' —', and '— '.
• Intermediate Steps: Included all tokens containing the em dash, including surrounding context like spaces and characters touching the dash.
• Final Stage: Extended to cover hyphens and en dashes, which the AI tends to substitute for em dashes, assigning a -100 bias to 106 tokens that encompass all these variants.

Testing across different models—OpenAI’s GPT-4 and its derivatives—revealed that this exhaustive biasing rendered the AI largely resistant to inserting unwanted dash characters. Despite initial concerns that such aggressive token suppression might harm response fluency or creativity, manual evaluations showed minimal impact on answer quality, especially in models with more nuanced language understanding.

For those interested in replicating this approach, a Python script along with the list of biased tokens is available [here](https://gist.github.com/kernkraft235/101aa4c03c43c