Harnessing the API’s “logit_bias” Parameter to Minimize Em Dashes: My Experience Suppressing 106 Tokens and a Code Guide for a “Dash-Free” Response Test

Achieving Explicit Control Over Em Dashes in GPT-4 Using API Logit Bias

In the quest to refine how AI models handle punctuation, especially the problematic em dash (—), many developers have explored advanced techniques to influence language outputs. One such method involves leveraging the logit_bias parameter via OpenAI’s API, which provides granular control over token probabilities.

The Challenge with Em Dashes

Despite efforts to instruct models not to use em dashes—via custom instructions, memory adjustments, or prompt engineering—GPT models often revert to their default behavior, inserting these punctuation marks frequently. This inconsistency poses a challenge for content creators aiming for uniform stylistic output.

Innovative Solution: Biasing Tokens to Suppress Em Dashes

A promising workaround involves analyzing the tokenization process. Tokens for symbols like the em dash and similar characters (e.g., en dashes, hyphens) are often represented by distinct token IDs. By applying a strong negative bias (e.g., -100) to these token IDs, we can significantly diminish their likelihood of appearing in generated text.

Practical Implementation and Findings

Here’s an overview of the approach:

• Start by identifying all tokens that include or represent the em dash, en dash, hyphen, and related punctuation.

• Gradually increase the number of tokens biased against. For example:

• Initially target tokens explicitly representing ‘—’ (em dash).

• Extend biases to tokens containing ‘—’ with surrounding letters.

• Include tokens for en dashes and hyphens, especially when models switch to alternative dash characters to circumvent biases.

• It typically requires setting biases on over 100 tokens—up to 106 in some cases—to effectively suppress em dash usage. This rigorous biasing can alter responses without significantly impairing the model’s overall coherence or accuracy.

Results from Testing and Evaluation

Experiments with various models including GPT-4, GPT-4.1, and smaller variants demonstrated that:

• Responses containing actual em dashes decreased markedly.
• Alternative dash characters (like hyphens or en dashes) sometimes emerged as replacements when biases were insufficient.
• Complete suppression of the dash is achievable with aggressive biasing, and surprisingly, the models maintain good response quality.

Sample Comparison

For example, when prompted to give a “hot take” on productivity:

• Without biasing: The model often uses em dashes for emphasis.
• With biasing: The response replaces em dashes with commas or