Exploring the “logit_bias” Parameter in the API: Combatting Em Dashes and Suppressing 106 Tokens – My Results and Sample Code for a “Dash-Free” Response Test

Mastering Em Dash Suppression in OpenAI’s API: An In-Depth Guide

Are you frustrated with persistent em dashes appearing in your GPT-generated content despite numerous tweaks? If so, you’re not alone. Many developers and writers grapple with controlling specific tokens, especially the elusive em dash (—), when using OpenAI’s API. Here’s an exploration of effective techniques—culminating in a surprisingly robust approach—that might just help you tame those unwelcome dashes.

The Challenge of Em Dashes

Getting GPT models to avoid em dashes isn’t straightforward. Traditional methods—such as custom instructions or memory—rarely suffice because language models naturally prefer certain stylistic choices, including the use of em dashes as punctuation. Adjusting output style, therefore, requires more nuanced control over token behavior.

Leveraging the logit_bias Parameter

A powerful, though brute-force, method involves the logit_bias parameter. This parameter allows us to assign a bias value between -100 and 100 to specific token IDs, effectively discouraging or encouraging their use during generation. The key is to identify all tokens related to the em dash and related punctuation, then heavily bias them against appearing.

Identifying and Suppressing Tokens

Since tokens can vary depending on context and tokenization—the process by which the model breaks down text into units—you have to account for multiple variations:

• The standard em dash: —
• Hyphen-minus: -
• En dash: –
• Combinations with surrounding spaces or characters

Here’s the strategic approach I adopted:

1. Start Small: Initially, set logit_bias for tokens directly representing —.
2. Expand Coverage: Broaden the bias to include tokens that contain — in their string, such as combined tokens with adjacent characters.
3. Address Variations: Include en dashes and hyphen-minus tokens.
4. Apply Heaviest Bias: To effectively eliminate these tokens, assign a bias of -100 (complete suppression).

Testing the Method

The results were telling. Initially, with just a few tokens suppressed, GPT still produced em dashes. It took suppressing over 100 tokens—up to 106 in total—for the model to largely eliminate em dashes. Here’s a quick summary of the progression:

• Suppress 3 tokens: —, " —", "— "
• Expand to roughly 40