DAM: Dynamic Attention Mask for Long-Context Large Language Model Inference Acceleration

ACL ARR 2025 February Submission7398 Authors

Abstract:

Long-context understanding is crucial for many NLP applications, yet transformers struggle with efficiency due to the quadratic complexity of self-attention. Sparse attention methods alleviate this cost but often impose static, predefined masks, failing to capture heterogeneous attention patterns. This results in suboptimal token interactions, limiting adaptability and retrieval accuracy in long-sequence tasks.
This work introduces a dynamic sparse attention mechanism that assigns adaptive masks at the attention-map level, preserving heterogeneous patterns across layers and heads. Unlike existing approaches, our method eliminates the need for fine-tuning and predefined mask structures while maintaining computational efficiency. By learning context-aware attention structures, it achieves high alignment with full-attention models, ensuring minimal performance degradation while reducing memory and compute overhead.
This approach provides a scalable alternative to full attention, enabling the practical deployment of large-scale Large Langue Models (LLMs) without sacrificing retrieval performance.

Paper Type: Long

Research Area: Efficient/Low-Resource Methods for NLP

Research Area Keywords: Sparse Attention, Model Efficiency, Large Language Models

Contribution Types: Approaches to low-resource settings, Approaches low compute settings-efficiency

Languages Studied: English, Chinese

Submission Number: 7398