Positional Interpolation
Definition
Position interpolation scales positions for processing longer inputs.
A method to extend the context window that scales position indices into the range seen during training.
Simply explained
More positions are mapped into the already known position range to process longer sequences.
Why it matters in practice
The term helps to select, develop, evaluate AI systems in a well-founded manner and realistically assess their technical risks.
Example from business
A model is adapted to longer sequences with scaled position indices.
Limits and pitfalls
- Equating a longer context window with reliably used context.
Origin & history
Chen and co-authors published Position Interpolation in 2023 for RoPE-based language models.
Use in mid-sized companies
- Possible application: A model is adapted to longer sequences with scaled position indices.
Frequently asked questions
What does position interpolation mean?
A method to extend the context window that scales position indices into the range seen during training. What mistakes should be avoided? Equating a longer context window with reliably used context.
Sources
Source checked on 10.10.2026. Editorial application examples are hypothetical; no proof of real customer results.
Editorially reviewed on: