this is a technique to increase the llms context window size without fine tuning it

Position interpolation is a technique used to extend the context window of Transformer-based language models—especially those using Rotary Position Embeddings (RoPE)—without retraining from scratch.

Instead of allowing position indices to exceed the maximum length seen during training (which causes unstable attention scores due to extrapolation), position interpolation scales down the position indices of longer sequences so they fit within the original training range.

For example, if a model was trained with a maximum of 2,048 tokens but needs to process 8,192, each position is multiplied by a scaling factor (e.g., 2048/8192 = 0.25). So, position 8,192 becomes 2,048 after scaling.

https://arxiv.org/pdf/2306.15595 (original paper)