ModelRefs / Positional Bias — AI Glossary
Positional Bias — AI Glossary
A model's tendency to favor answer options at specific positions (e.g., first, last) independent of content quality.
Overview
Positional bias affects multiple-choice QA (models prefer option A or the last option) and long-context retrieval (primacy/recency effects). Can inflate benchmark scores if tests are not position-balanced. Mitigated by ensembling across position permutations or using calibration techniques.
Reference details
| Topic | prompting |
|---|---|
| Last reviewed | 2026-06-24 |
Related terms
Commonly confused with
Positional bias is the general effect — content is judged partly by where it sits. Recency bias is one specific case, the tail of the context being over-weighted; primacy is the other end; lost-in-the-middle is the observation that both ends beat the middle. Naming the specific position matters, because the mitigation differs: shuffling option order addresses one, reordering retrieved context addresses another.
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Frequently asked questions
What is Positional Bias?
A model's tendency to favor answer options at specific positions (e.g., first, last) independent of content quality.
What concepts are related to Positional Bias?
Closely related concepts include lost in the middle, calibration, recency bias.