Variation 45: How I Leveraged the “logit_bias” Parameter to Reduce Em Dashes—Suppressing 106 Tokens and My Results with a “No Dash̶ Approach and Sample Code

ChatGPT GAIadmin July 28, 2025 0 Comments

Variation 45: How I Leveraged the “logit_bias” Parameter to Reduce Em Dashes—Suppressing 106 Tokens and My Results with a “No Dash̶ Approach and Sample Code

Controlling Em Dashes in ChatGPT via API Token Biasing: A Practical Experiment

For many users working with OpenAI’s API, a common annoyance is the persistent appearance of em dashes (—) in generated responses. Despite attempts with custom instructions and memory management, eliminating these punctuation marks can be surprisingly difficult. Recently, I discovered a straightforward method: leveraging the logit_bias parameter in the API to suppress em dashes and related tokens effectively.

The Challenge

Em dashes often pop up unexpectedly, regardless of instructions not to use them. This behavior stems from how the model tokenizes and generates output, with multiple tokens and symbols that can represent or produce dash characters. To address this, I focused on directly influencing token probability distributions during generation.

The Solution: Setting Logit Biases

The logit_bias parameter allows you to assign a bias score ranging from -100 to +100 to specific token IDs. Applying a bias of -100 essentially excludes these tokens from the model’s choices. My strategy involved:
– Identifying tokens that generate or contain em dashes.
– Applying a -100 bias to these tokens to discourage their use.

Initially, I targeted tokens directly representing the em dash character —. However, I quickly realized that the model can produce variants such as en dashes, hyphens, or combine characters to create dash-like symbols. Therefore, I expanded the biasing to include all tokens containing related characters and patterns that could result in dash output.

The Process in Action

Here’s a summary of the steps I took:
1. Identified all relevant tokens involving —, en dashes, hyphens, and similar symbols.
2. Incrementally increased the number of tokens biased until the model stopped generating unwanted dash characters.
3. At 106 tokens biased with -100 each, the model’s propensity to produce em dashes was effectively suppressed.

Results & Observations

I experimented with various sample prompts, comparing responses with and without the biases applied. Notably:
– With minimal biasing, the model frequently included em dashes and hyphen variants.
– At 106 biased tokens, the responses were free of em dashes, albeit with some impact on sentence structure or style occasionally.
– Interestingly, the response preferences of different models varied, with some showing a mild bias towards hyphenated forms, but overall, the suppression was significant.