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Enhancing RWKV-based Language Models for Long-Sequence Text Generation

Xinghan Pan

TL;DR

This work tackles the challenge of long-context language modeling by enhancing the RWKV architecture with adaptive temporal gating and a position-aware token shift. By introducing a neurally-guided gating mechanism and a shifted hidden-state integration, the model dynamically modulates past information to improve coherence without sacrificing linear time complexity. Empirical results show substantial gains in ROUGE-L and BLEU with only a minor increase in latency, and ablation studies confirm the contributions of both components. The approach offers a practical avenue for scalable, long-sequence text generation and points to future enhancements such as hierarchical gating and retrieval-augmented generation.

Abstract

This paper introduces an enhanced RWKV architecture with adaptive temporal gating mechanisms for improved long-context language modeling. We propose two principal innovations: (1) a position-aware convolutional shift operator that captures local syntactic patterns while preserving global coherence, and (2) a neurally-gated information routing mechanism that dynamically regulates inter-token information flow. Through comprehensive experiments on text generation tasks, our enhanced model demonstrates superior performance compared to the baseline RWKV, achieving 96.5 relative improvement in ROUGE-L scores with only 2.95 increased inference latency. Ablation studies validate the individual contributions of each component, while linguistic analysis reveals the model's adaptive attention to syntactic boundaries and entity coherence. The proposed modifications maintain RWKV's linear computational complexity while significantly enhancing its contextual modeling capabilities, establishing new state-of-the-art performance for recurrent-style architectures in long-form text generation.

Enhancing RWKV-based Language Models for Long-Sequence Text Generation

TL;DR

This work tackles the challenge of long-context language modeling by enhancing the RWKV architecture with adaptive temporal gating and a position-aware token shift. By introducing a neurally-guided gating mechanism and a shifted hidden-state integration, the model dynamically modulates past information to improve coherence without sacrificing linear time complexity. Empirical results show substantial gains in ROUGE-L and BLEU with only a minor increase in latency, and ablation studies confirm the contributions of both components. The approach offers a practical avenue for scalable, long-sequence text generation and points to future enhancements such as hierarchical gating and retrieval-augmented generation.

Abstract

This paper introduces an enhanced RWKV architecture with adaptive temporal gating mechanisms for improved long-context language modeling. We propose two principal innovations: (1) a position-aware convolutional shift operator that captures local syntactic patterns while preserving global coherence, and (2) a neurally-gated information routing mechanism that dynamically regulates inter-token information flow. Through comprehensive experiments on text generation tasks, our enhanced model demonstrates superior performance compared to the baseline RWKV, achieving 96.5 relative improvement in ROUGE-L scores with only 2.95 increased inference latency. Ablation studies validate the individual contributions of each component, while linguistic analysis reveals the model's adaptive attention to syntactic boundaries and entity coherence. The proposed modifications maintain RWKV's linear computational complexity while significantly enhancing its contextual modeling capabilities, establishing new state-of-the-art performance for recurrent-style architectures in long-form text generation.

Paper Structure

This paper contains 19 sections, 4 equations, 3 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Sparse Attention Mechanisms
  4. Memory-Augmented Networks
  5. Linear Transformers
  6. Model Architecture
  7. RWKV Foundation
  8. Proposed Enhancement: Adaptive Token Shift and Gating Mechanism
  9. Experimental Setup
  10. Datasets
  11. Evaluation Metrics
  12. Baseline and Ablation Models
  13. Results
  14. Forward Propagation Time
  15. Text Generation Quality
  16. ...and 4 more sections

Figures (3)

  • Figure 1: Flowchart of the Enhanced Model Architecture.
  • Figure 2: Comparison of Forward Propagation Times across Different Models.
  • Figure 3: Comparison of BLEU and ROUGE Scores between Base and Enhanced Models.