Resonance RoPE: Improving Context Length Generalization of Large
  Language Models
Suyuchen Wang Ivan Kobyzev Peng Lu Mehdi Rezagholizadeh Bang Liu
This paper addresses the challenge of train-short-test-long (TSTL) scenarios
in Large Language Models (LLMs) equipped with Rotary Position Embedding (RoPE),
where models pre-trained on shorter sequences face difficulty with
out-of-distribution (OOD) token positions in longer sequences. We introduce
Resonance RoPE, a novel approach designed to narrow the generalization gap in
TSTL scenarios by refining the interpolation of RoPE features for OOD
positions, significantly improving the model performance without additional
online computational costs. Furthermore, we present PosGen, a new synthetic
benchmark specifically designed for fine-grained behavior analysis in TSTL
scenarios, aiming to isolate the constantly increasing difficulty of token
generation on long contexts from the challenges of recognizing new token
positions. Our experiments on synthetic tasks show that after applying
Resonance RoPE, Transformers recognize OOD position better and more robustly.
Our extensive LLM experiments also show superior performance after applying
Resonance RoPE to the current state-of-the-art RoPE scaling method, YaRN, on
both upstream language modeling tasks and a variety of downstream long-text
applications.