Logit-Bias
Definition
Logit bias alters the raw scores of certain tokens before sampling, thereby influencing their selection probability.
Depending on the API or implementation, positive or negative values are added to token logits. This is a controlling intervention in decoding, not a guarantee of a specific outcome.
Simply explained
How to give individual words bonus or penalty points before selection.
Why it matters in practice
Relevant for special generation controls but error-prone due to tokenization.
Example from business
A system lowers the score of an undesired token and checks whether semantic workarounds still occur.
Limits and pitfalls
- Think in text instead of token IDs; build hard security with logit bias; ignore compound tokens.
Origin & history
Logit manipulation is a general decoding technique; different model APIs provide different interfaces for it.
Use in mid-sized companies
- Possible application in SMEs: Use only for narrowly defined output preferences, not as a security control.
Frequently asked questions
Can logit bias reliably forbid a word?
Not reliable due to tokenization and reformulations.
Sources
- Chat Completions, logit_bias · OpenAI API, Chat Completions, logit_bias
- Using logit bias · OpenAI, Using logit bias
Sources checked on 12.10.2026.
Editorially reviewed on: