Exploring the “logit_bias” Parameter in the API: How I Reduced Em Dashes and Suppressed 106 Tokens—Findings and Code for a “No Dash” Response Test

Mastering Em Dashes in AI Language Models: A Practical Guide to Suppressing Unwanted Hyphenation

Many AI enthusiasts and developers have wrestled with the persistent issue of em dashes—those long, elegant punctuation marks—that often appear unexpectedly in generated text. Despite attempts with custom instructions and memory editing, removing these dashes can be surprisingly challenging. A recent experiment using OpenAI’s API revealed a surprisingly effective approach: leveraging the logit_bias parameter to suppress em dashes and related tokens by assigning a strong negative bias.

The Challenge

Getting language models like GPT-4 to avoid em dashes isn’t straightforward. Tokens for symbols like the em dash (—) and hyphen (-) can merge or be substituted in unpredictable ways, especially when the model tries to preserve certain stylistic nuances. Traditional methods—like instructing the model to avoid dash characters—often fall short because the model can still generate them based on context or tokenization quirks.

The Logit Bias Solution

The key insight was to use the logit_bias parameter, which adjusts the likelihood of specific tokens during generation. By assigning a bias range from -100 to 100, developers can significantly discourage the model from selecting certain tokens.

The process involved:

1. Identifying all token IDs related to em dashes and their variants.
2. Applying a bias of -100 to these tokens, making their use highly unlikely.
3. Recognizing that the model might still fallback to similar tokens like en dashes or hyphens, requiring additional tokens to be biased as well.

Iterative Biasing Strategy

The progression of the experiment was as follows:

• Starting with as few as 3 tokens, the model still used em dashes.
• Increasing the bias to 40 tokens targeting all tokens with “—” in them.
• Extending to 62 tokens where the model shifted to en dashes.
• Finally, raising the bias to 106 tokens covering hyphens used as em dashes and other variants, effectively suppressing the dash behavior entirely.

Sample Evaluation

Using manual prompts, the impact was clear:

• Normal responses contained em dashes, reflecting typical stylistic choices.
• Biased responses with logit_bias applied showed a notable decrease in dash usage, favoring more conventional punctuation or phrases.

Practical Implications

This approach demonstrates that even a blunt-force method—biasing