ModelRefs / Recency Bias — AI Glossary
Recency Bias — AI Glossary
A model's tendency to weight information appearing late in the context more heavily than earlier content.
Overview
Recency bias is a manifestation of positional bias in transformer attention—later tokens receive stronger gradient signal in autoregressive training. Affects multi-turn conversations (overweighting recent turns), RAG (favoring last-inserted chunks), and long-document summarization. Addressed by strategic context ordering and multi-position ensemble prompting.
Reference details
| Topic | prompting |
|---|---|
| Last reviewed | 2026-06-24 |
Related terms
Commonly confused with
One particular positional bias: late content outweighs early content. That is why an instruction repeated at the end of a long prompt is often obeyed when the same instruction at the top is not, and why appending retrieved context after the question tends to beat prepending it. It is not the same as lost-in-the-middle, which says the middle loses to *both* ends.
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Frequently asked questions
What is Recency Bias?
A model's tendency to weight information appearing late in the context more heavily than earlier content.
What concepts are related to Recency Bias?
Closely related concepts include positional bias, lost in the middle, context management.