Using the “logit_bias” Parameter in the API to Combat Em Dashes: My Experience Suppressing 106 Tokens and a Guide to Creating “No Dash” Responses

Enhancing OpenAI API Responses: How Suppressing Em Dashes Can Lead to Cleaner Output

If you’ve ever struggled with unwanted em dashes sneaking into your AI-generated content, you’re not alone. Many developers and content creators find that despite various prompts and instructions, the AI persistently inserts em dashes—sometimes at the most inconvenient moments. Recently, I discovered a surprisingly effective method to mitigate this behavior by leveraging the logit_bias parameter in the OpenAI API.

Understanding the Challenge

The core issue is that, by default, language models often prefer certain punctuation tokens—like em dashes—for stylistic reasons. Attempts to instruct the AI to avoid em dashes through custom prompts or memory modifications frequently fall short. The problem is compounded by the fact that tokens such as em dashes can be represented in multiple forms or combined with other characters, making them elusive targets.

Implementing a Technical Solution

My approach involved identifying the token IDs associated with em dashes and related punctuation. Initially, I targeted the primary em dash (—), but I quickly realized that the model often substitutes similar tokens, such as en dashes or hyphens, to preserve the “spirit” of the dash.

To effectively suppress these, I used the logit_bias parameter, assigning a bias of -100 to a comprehensive list of tokens associated with dashes, hyphens, and their variants. It took setting biases on over 100 tokens—specifically 106—to significantly diminish their appearance in the output.

Here’s an overview of the process:
– Starting with the direct em dash and related tokens touching it.
– Expanding the biasing to include tokens with en dashes and hyphens in different contexts.
– Applying a strict bias to hyphen tokens that are not adjacent to alphabetic characters, treating them as undesirable for dash usage.

Evaluating the Impact

I tested this method across different models, primarily using the latest GPT-4 variants. The results showed a clear trend:
– The responses increasingly avoided em dashes when biased tokens were suppressed.
– Models with more advanced capabilities (like GPT-4) showed a preference for replacing dashes with alternative punctuation or phrasing.
– Even with a blunt-force approach—biasing over 100 tokens—the integrity of the generated responses remained intact, and the overall response quality was preserved or improved in some cases.

Sample Comparison

Consider two responses to a prompt asking for a “hot take” on productivity obsession:

**