Scaling Laws of RoPE-based Extrapolation

Published: 16 Jan 2024, Last Modified: 14 Mar 2024ICLR 2024 posterEveryoneRevisionsBibTeX
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Keywords: Position Embeddin, Length Extrapolation, Large Language Model, Natural Language Processing
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TL;DR: In this work, we propose a unified framework from the prospective of period, to explain the mechanism of RoPE-based extrapolation by whether increasing or decreasing the rotary base.
Abstract: The extrapolation capability of Large Language Models (LLMs) based on Rotary Position Embedding \citep{su2021roformer} is currently a topic of considerable interest. The mainstream approach to addressing extrapolation with LLMs involves modifying RoPE by replacing 10000, the rotary base of $\theta_n={10000}^{-2n/d}$ in the original RoPE, with a larger value and providing longer fine-tuning text. In this work, we first observe that fine-tuning a RoPE-based LLM with either a smaller or larger base in pre-training context length could significantly enhance its extrapolation performance. After that, we propose \textbf{\textit{Scaling Laws of RoPE-based Extrapolation}}, a unified framework from the periodic perspective, to describe the relationship between the extrapolation performance and base value as well as tuning context length. In this process, we also explain the origin of the RoPE-based extrapolation issue by \textbf{\textit{critical dimension for extrapolation}}. Besides these observations and analyses, we achieve extrapolation up to 1 million context length within only 16K training length on LLaMA2 7B and 13B \citep{touvron2023llama2}.
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Primary Area: general machine learning (i.e., none of the above)
Submission Number: 2660