Round and Round We Go! What makes Rotary Positional Encodings useful?

• URL: http://arxiv.org/abs/2410.06205v1
• Date: Tue, 8 Oct 2024 17:07:01 GMT
• Title: Round and Round We Go! What makes Rotary Positional Encodings useful?
• Authors: Federico Barbero, Alex Vitvitskyi, Christos Perivolaropoulos, Razvan Pascanu, Petar Veličković,
• Abstract summary: We study the internals of a trained Gemma 7B model to understand how RoPE is being used at a mechanical level. We find that Gemma learns to use RoPE to construct robust "positional" attention patterns by exploiting the highest frequencies. We propose a modification of RoPE that fixes some highlighted issues and improves performance.
• Score: 15.543752938828831
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Positional Encodings (PEs) are a critical component of Transformer-based Large Language Models (LLMs), providing the attention mechanism with important sequence-position information. One of the most popular types of encoding used today in LLMs are Rotary Positional Encodings (RoPE), that rotate the queries and keys based on their relative distance. A common belief is that RoPE is useful because it helps to decay token dependency as relative distance increases. In this work, we argue that this is unlikely to be the core reason. We study the internals of a trained Gemma 7B model to understand how RoPE is being used at a mechanical level. We find that Gemma learns to use RoPE to construct robust "positional" attention patterns by exploiting the highest frequencies. We also find that, in general, Gemma greatly prefers to use the lowest frequencies of RoPE, which we suspect are used to carry semantic information. We mathematically prove interesting behaviours of RoPE and conduct experiments to verify our findings, proposing a modification of RoPE that fixes some highlighted issues and improves performance. We believe that this work represents an interesting step in better understanding PEs in LLMs, which we believe holds crucial value for scaling LLMs to large sizes and context lengths.

Related papers

• Understanding the RoPE Extensions of Long-Context LLMs: An Attention Perspective [35.947737679664016]
  This paper offers a straightforward yet in-depth understanding of RoPE extensions from an attention perspective. Using longer continual pretraining lengths for RoPE extensions could reduce attention uncertainty and significantly enhance extrapolation.
  arXiv  Detail & Related papers  (2024-06-19T07:23:33Z)
• Base of RoPE Bounds Context Length [37.11078116104313]
  Rotary position embedding (RoPE) is a technique that encodes the position information with a rotation matrix. In this paper, we find that LLMs may obtain a superficial long-context ability based on the OOD theory. Our work reveals the relationship between context length and RoPE base both theoretically and empirically, which may shed light on future long context training.
  arXiv  Detail & Related papers  (2024-05-23T14:03:31Z)
• Rotary Position Embedding for Vision Transformer [44.27871591624888]
  This study provides a comprehensive analysis of Rotary Position Embedding (RoPE) when applied to Vision Transformer (ViT) RoPE demonstrates impressive extrapolation performance, i.e., maintaining precision while increasing image resolution at inference. It eventually leads to performance improvement for ImageNet-1k, COCO detection, and ADE-20k segmentation.
  arXiv  Detail & Related papers  (2024-03-20T04:47:13Z)
• Feedback Loops With Language Models Drive In-Context Reward Hacking [78.9830398771605]
  We show that feedback loops can cause in-context reward hacking (ICRH) We identify and study two processes that lead to ICRH: output-refinement and policy-refinement. As AI development accelerates, the effects of feedback loops will proliferate.
  arXiv  Detail & Related papers  (2024-02-09T18:59:29Z)
• Scaling Laws of RoPE-based Extrapolation [103.33995311915864]
  We propose textbftextitScaling Laws of RoPE-based Extrapolation to describe the relationship between the extrapolation performance and base value. We achieve extrapolation up to 1 million context length within only 16K training length on LLaMA2 7B and 13B.
  arXiv  Detail & Related papers  (2023-10-08T15:50:36Z)
• The Impact of Positional Encoding on Length Generalization in Transformers [50.48278691801413]
  We compare the length generalization performance of decoder-only Transformers with five different position encoding approaches. Our findings reveal that the most commonly used positional encoding methods, such as ALiBi, Rotary, and APE, are not well suited for length generalization in downstream tasks.
  arXiv  Detail & Related papers  (2023-05-31T00:29:55Z)
• SPE-Net: Boosting Point Cloud Analysis via Rotation Robustness Enhancement [118.20816888815658]
  We propose a novel deep architecture tailored for 3D point cloud applications, named as SPE-Net. The embedded Selective Position variant' procedure relies on an attention mechanism that can effectively attend to the underlying rotation condition of the input. We demonstrate the merits of the SPE-Net and the associated hypothesis on four benchmarks, showing evident improvements on both rotated and unrotated test data over SOTA methods.
  arXiv  Detail & Related papers  (2022-11-15T15:59:09Z)
• Rethinking and Improving Relative Position Encoding for Vision Transformer [61.559777439200744]
  Relative position encoding (RPE) is important for transformer to capture sequence ordering of input tokens. We propose new relative position encoding methods dedicated to 2D images, called image RPE (iRPE)
  arXiv  Detail & Related papers  (2021-07-29T17:55:10Z)
• RoFormer: Enhanced Transformer with Rotary Position Embedding [9.01819510933327]
  We propose a novel method named Rotary Position Embedding(RoPE) to effectively leverage the positional information. RoPE encodes the absolute position with a rotation matrix and meanwhile incorporates the explicit relative position dependency in self-attention formulation. We evaluate the enhanced transformer with rotary position embedding, also called RoFormer, on various long text classification benchmark datasets.
  arXiv  Detail & Related papers  (2021-04-20T09:54:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.