Leveraging the API’s “logit_bias” Parameter to Combat Em Dashes: My Experience Suppressing 106 Tokens & the Code for a “No Dash” Response Test

Harnessing the Power of logit_bias in the OpenAI API to Minimize Em Dashes: A Practical Guide

If you’ve ever grappled with unwanted em dashes creeping into your AI-generated text, you’re not alone. For many users, eliminating these typographical quirks can be surprisingly challenging despite numerous attempts with custom instructions and memory settings. Recently, I discovered an effective (though blunt) technique using the logit_bias parameter in OpenAI’s API—specifically, setting the bias for tokens representing em dashes and similar punctuation to strongly discourage their usage.

The Challenge of Em Dashes

Despite specifying instructions to avoid em dashes, models often persist, especially when they see tokens like —, en dashes –, or hyphens - used in certain contexts. These characters are tricky because they can be tokenized in multiple ways: a single token, part of a bigger token, or even combined with other characters, making suppression more complex than a simple bias.

A Systematic Suppression Approach

My goal was to minimize em dash appearances entirely. To do this, I examined the tokenization process and identified all instances where the model might produce an em dash or similar punctuation. I then assigned a bias of -100 (the strongest negative bias) to these tokens. Here’s the progression I followed:

• Initially: Target individual tokens like '—' and ' —'.
• Next: Expand to include all tokens containing the em dash.
• Further: Address tokens that use en dashes or hyphens, which can be confused or substituted.
• Finally: Apply bias to hyphens not touching letters, considering their common substitution as em dashes.

In total, 106 tokens needed to be biased heavily to suppress undesired dash characters. While it sounds aggressive, this approach effectively “patched over” the model’s preferred pathways.

Results and Observations

In experimental prompts—such as requesting a “hot take” or a “pragmatic solution” to national division—responses without suppression often included em dashes or hyphens used as em dashes. After applying the 106-token bias, responses significantly reduced or eliminated these punctuation marks. Notably:

• Responses from certain models (like GPT-4o-latest) shifted from favoring traditional dash responses to more neutral wording.
• Imposing this kind of bias did not appreciably harm the coherence or quality of