RRWKV: Capturing Long-range Dependencies in RWKV

• URL: http://arxiv.org/abs/2306.05176v4
• Date: Fri, 13 Sep 2024 08:58:47 GMT
• Title: RRWKV: Capturing Long-range Dependencies in RWKV
• Authors: Leilei Wang,
• Abstract summary: The paper devises the Retrospected Receptance Weighted Key Value architecture via incorporating the retrospecting ability into the RWKV to effectively absorb information. RWKV has exploited a linearly tensor-product attention mechanism and achieved parallelized computations by deploying the time-sequential mode.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Owing to the impressive dot-product attention, the Transformers have been the dominant architectures in various natural language processing (NLP) tasks. Recently, the Receptance Weighted Key Value (RWKV) architecture follows a non-transformer architecture to eliminate the drawbacks of dot-product attention, where memory and computational complexity exhibits quadratic scaling with sequence length. Although RWKV has exploited a linearly tensor-product attention mechanism and achieved parallelized computations by deploying the time-sequential mode, it fails to capture long-range dependencies because of its limitation on looking back at previous information, compared with full information obtained by direct interactions in the standard transformer. Therefore, the paper devises the Retrospected Receptance Weighted Key Value (RRWKV) architecture via incorporating the retrospecting ability into the RWKV to effectively absorb information, which maintains memory and computational efficiency as well.

Related papers

• Sentinel: Multi-Patch Transformer with Temporal and Channel Attention for Time Series Forecasting [48.52101281458809]
  Transformer-based time series forecasting has recently gained strong interest due to the ability of transformers to model sequential data. We propose Sentinel, a transformer-based architecture composed of an encoder able to extract contextual information from the channel dimension. We introduce a multi-patch attention mechanism, which leverages the patching process to structure the input sequence in a way that can be naturally integrated into the transformer architecture.
  arXiv  Detail & Related papers  (2025-03-22T06:01:50Z)
• Tensor Product Attention Is All You Need [54.40495407154611]
  Product Attention (TPA) is a novel attention mechanism that uses tensor decompositions to represent queries, keys, and values compactly. TPA achieves improved model quality alongside memory efficiency. We introduce the ProducT ATTion Transformer (T6), a new model architecture for sequence modeling.
  arXiv  Detail & Related papers  (2025-01-11T03:37:10Z)
• A Survey of RWKV [16.618320854505786]
  Receptance Weighted Key Value (RWKV) model offers a novel alternative to the Transformer architecture. Unlike conventional Transformers, which depend heavily on self-attention, RWKV adeptly captures long-range dependencies with minimal computational demands. This paper seeks to fill this gap as the first comprehensive review of the RWKV architecture, its core principles, and its varied applications.
  arXiv  Detail & Related papers  (2024-12-19T13:39:24Z)
• Enhanced Computationally Efficient Long LoRA Inspired Perceiver Architectures for Auto-Regressive Language Modeling [2.9228447484533695]
  The Transformer architecture has revolutionized the Natural Language Processing field and is the backbone of Large Language Models (LLMs) One of the challenges in the Transformer architecture is the quadratic complexity of the attention mechanism that prohibits the efficient processing of long sequence lengths. One of the important works in this respect is the Perceiver class of architectures that have demonstrated excellent performance while reducing the computation complexity.
  arXiv  Detail & Related papers  (2024-12-08T23:41:38Z)
• RecurFormer: Not All Transformer Heads Need Self-Attention [14.331807060659902]
  Transformer-based large language models (LLMs) excel in modeling complex language patterns but face significant computational costs during inference. We propose RecurFormer, a novel architecture that replaces certain attention heads with linear recurrent neural networks.
  arXiv  Detail & Related papers  (2024-10-10T15:24:12Z)
• DAPE V2: Process Attention Score as Feature Map for Length Extrapolation [63.87956583202729]
  We conceptualize attention as a feature map and apply the convolution operator to mimic the processing methods in computer vision. The novel insight, which can be adapted to various attention-related models, reveals that the current Transformer architecture has the potential for further evolution.
  arXiv  Detail & Related papers  (2024-10-07T07:21:49Z)
• PRformer: Pyramidal Recurrent Transformer for Multivariate Time Series Forecasting [82.03373838627606]
  Self-attention mechanism in Transformer architecture requires positional embeddings to encode temporal order in time series prediction. We argue that this reliance on positional embeddings restricts the Transformer's ability to effectively represent temporal sequences. We present a model integrating PRE with a standard Transformer encoder, demonstrating state-of-the-art performance on various real-world datasets.
  arXiv  Detail & Related papers  (2024-08-20T01:56:07Z)
• An All-MLP Sequence Modeling Architecture That Excels at Copying [6.824179106436217]
  We present an all-MLP sequence modeling architecture that can match Transformers on the copying task. In ablation study, we found both exponential activation and pre-activation normalization are indispensable for Transformer-level copying.
  arXiv  Detail & Related papers  (2024-06-23T17:19:26Z)
• Folded Context Condensation in Path Integral Formalism for Infinite Context Transformers [0.0]
  We present a generalized formulation of the Transformer algorithm by reinterpreting its core mechanisms within the framework of Path Integral formalism. We obtain a more compact and efficient representation, in which the contextual information of a sequence is condensed into memory-like segments. We validate the effectiveness of this approach through the Passkey retrieval task and a summarization task, demonstrating that the proposed method preserves historical information while exhibiting memory usage that scales linearly with sequence length.
  arXiv  Detail & Related papers  (2024-05-07T19:05:26Z)
• Vision-RWKV: Efficient and Scalable Visual Perception with RWKV-Like Architectures [99.20299078655376]
  This paper introduces Vision-RWKV, a model adapted from the RWKV model used in the NLP field. Our model is designed to efficiently handle sparse inputs and demonstrate robust global processing capabilities. Our evaluations demonstrate that VRWKV surpasses ViT's performance in image classification and has significantly faster speeds and lower memory usage.
  arXiv  Detail & Related papers  (2024-03-04T18:46:20Z)
• RWKV: Reinventing RNNs for the Transformer Era [54.716108899349614]
  We propose a novel model architecture that combines the efficient parallelizable training of transformers with the efficient inference of RNNs. We scale our models as large as 14 billion parameters, by far the largest dense RNN ever trained, and find RWKV performs on par with similarly sized Transformers.
  arXiv  Detail & Related papers  (2023-05-22T13:57:41Z)
• Resource-Efficient Separation Transformer [14.666016177212837]
  This paper explores Transformer-based speech separation with a reduced computational cost. Our main contribution is the development of the Resource-Efficient Separation Transformer (RE-SepFormer), a self-attention-based architecture. The RE-SepFormer reaches a competitive performance on the popular WSJ0-2Mix and WHAM! datasets in both causal and non-causal settings.
  arXiv  Detail & Related papers  (2022-06-19T23:37:24Z)
• ReconFormer: Accelerated MRI Reconstruction Using Recurrent Transformer [60.27951773998535]
  We propose a recurrent transformer model, namely textbfReconFormer, for MRI reconstruction. It can iteratively reconstruct high fertility magnetic resonance images from highly under-sampled k-space data. We show that it achieves significant improvements over the state-of-the-art methods with better parameter efficiency.
  arXiv  Detail & Related papers  (2022-01-23T21:58:19Z)
• CSformer: Bridging Convolution and Transformer for Compressive Sensing [65.22377493627687]
  This paper proposes a hybrid framework that integrates the advantages of leveraging detailed spatial information from CNN and the global context provided by transformer for enhanced representation learning. The proposed approach is an end-to-end compressive image sensing method, composed of adaptive sampling and recovery. The experimental results demonstrate the effectiveness of the dedicated transformer-based architecture for compressive sensing.
  arXiv  Detail & Related papers  (2021-12-31T04:37:11Z)

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.