logit bias

**Logit bias** is the **token-level decoding control that adds positive or negative score offsets to specific tokens before sampling or search** - it enables fine-grained steering of lexical output behavior. **What Is Logit bias?** - **Definition**: Manual adjustment applied directly to token logits at inference time. - **Bias Direction**: Positive values encourage token selection and negative values suppress it. - **Granularity**: Targets individual tokens, including control symbols and keywords. - **Scope**: Used in constrained generation, safety controls, and format enforcement workflows. **Why Logit bias Matters** - **Behavior Steering**: Allows direct influence over token choices without retraining. - **Policy Enforcement**: Can reduce likelihood of disallowed terms or patterns. - **Format Reliability**: Boosts required delimiters or field markers in structured outputs. - **Rapid Iteration**: Supports runtime experimentation with minimal deployment overhead. - **Risk Control**: Fine-tunes output tendencies for sensitive enterprise use cases. **How It Is Used in Practice** - **Token Mapping**: Resolve bias targets to tokenizer IDs for the exact model version. - **Magnitude Calibration**: Use small offsets first and escalate only with measured impact. - **Guarded Testing**: Validate side effects on fluency and semantic accuracy. Logit bias is **a precise runtime knob for token-level output control** - effective biasing requires careful calibration to avoid unintended distortion.