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

Eliminating Em Dashes in AI Responses: A Technical Deep Dive

Are you tired of em dashes creeping into your AI-generated content? I recently embarked on an experiment to curb this typographical nuisance using OpenAI’s API parameters, specifically leveraging the logit_bias feature. The goal? Suppress em dashes and similar punctuation artifacts effectively, without compromising response quality.

The Challenge of Em Dashes

Despite numerous attempts—custom instructions, memory configurations, and prompt engineering—em dashes stubbornly persisted in the outputs. Recognizing that tokenization nuances could be at play, I turned to the logit_bias parameter, which allows biasing token probabilities during model inference.

Strategy: Biasing Specific Tokens

Initially, I aimed to identify the token ID for the em dash (—). However, I soon realized that tokenization isn’t always straightforward; symbols can combine with adjacent characters to form other tokens, such as en dashes or hyphens. To comprehensively suppress all variants, I systematically increased the number of tokens biased negatively.

Here’s what transpired:

• Starting point: Biasing tokens directly containing —
• Expanded scope: Biasing tokens that include — in any context, covering variations with surrounding letters
• Further extension: Addressed en dashes and hyphens, especially cases where hyphens are used as em dashes
• Culmination: Suppressed 106 tokens—covering all common representations of dashes and hyphens

Implementation Results

The culmination was a significant reduction, requiring 106 tokens set to a bias of -100. This “crude force” approach suppressed the model’s preference for dash characters, converting outputs primarily away from using dashes altogether.

Below are example responses to the same prompt, illustrating the impact:

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

• Normal Response (no bias):

Here’s a hot take: The obsession with productivity culture is doing more harm than good—it’s glamorized burnout disguised as ambition…

• Biased Response (with logit_bias applied):

Here’s a hot take: the obsession with extreme productivity and hustle culture is just socially acceptable burnout glorified as ambition…

(Notice the absence of em dashes in the biased version)

Evaluations & Observations

Responses from various models demonstrated that this biasing approach predominantly yields