Leveraging the “logit_bias” Parameter in the API to Combat Em Dashes: My Experience with Suppressing 106 Tokens and a Guide to Creating Your Own “No Dash” Response Test

Overcoming Em Dashes in AI Text Generation: A Deep Dive into Token Biasing Techniques

Struggling with unwanted em dashes in AI-generated responses can be frustrating, especially when trying to achieve cleaner, dash-free prose. Recently, I embarked on an experiment using the OpenAI API’s logit_bias parameter to suppress these symbols effectively. Here’s a detailed overview of what I discovered, including practical code snippets, to help you replicate or adapt this approach for your projects.

The Challenge of Em Dashes

Despite multiple attempts—ranging from custom instructions to memory adjustments—eliminating em dashes from responses proved surprisingly challenging. The AI’s language model tends to generate these symbols because, at the token level, they can be represented in various ways, including as part of composite tokens or alternate dash styles such as en dashes or hyphens.

The Solution: Token Biasing

The key insight was leveraging the logit_bias parameter, which allows suppression or promotion of specific tokens by assigning bias scores between -100 and 100. Initially, I focused on identifying the token ID for the em dash (—). However, I soon realized that the model can generate similar characters—like en dashes or hyphens—by combining tokens or using different symbols. To cover all bases, I systematically biased multiple tokens associated with these characters.

Progressive Token Suppression

Here’s a summary of the incremental steps taken:

• 3 tokens: Targeted direct em dash tokens, including spaces and standalone symbols.

• 40 tokens: Extended to include all tokens containing the em dash symbol, accounting for adjacent characters.

• 62 tokens: Added suppression of en dashes and other similar characters resulting from tokenization.

• 106 tokens: Broadened suppression to hyphens used as em dashes, notably targeting hyphens not flanked by letters.

This comprehensive biasing ultimately suppressed over 100 tokens related to dashes, significantly reducing their appearance in generated responses.

Experimental Results

To evaluate the effectiveness, I tested various models and prompts. For example, a prompt requesting a “hot take” on productivity yielded divergent responses depending on biasing:

• Unbiased response: Openly used em dashes.

• Biased response: Replaced em dashes with alternatives like commas, colons, or descriptive phrasing, aligning with the suppression strategy.

Notably, models such as GPT-4 and its variants showed a preference toward avoiding dashes when