StableMask: Refining Causal Masking in Decoder-only Transformer
Qingyu Yin Xuzheng He Xiang Zhuang Yu Zhao Jianhua Yao Xiaoyu Shen Qiang Zhang
The decoder-only Transformer architecture with causal masking and relative
position encoding (RPE) has become the de facto choice in language modeling.
Despite its exceptional performance across various tasks, we have identified
two limitations: First, it requires all attention scores to be non-zero and sum
up to 1, even if the current embedding has sufficient self-contained
information. This compels the model to assign disproportional excessive
attention to specific tokens. Second, RPE-based Transformers are not universal
approximators due to their limited capacity at encoding absolute positional
information, which limits their application in position-critical tasks. In this
work, we propose StableMask: a parameter-free method to address both
limitations by refining the causal mask. It introduces pseudo-attention values
to balance attention distributions and encodes absolute positional information
via a progressively decreasing mask ratio. StableMask's effectiveness is
validated both theoretically and empirically, showing significant enhancements
in language models with parameter sizes ranging from 71M to 1.4B across diverse
datasets and encoding methods. We further show that it naturally supports (1)
efficient extrapolation without special tricks such as StreamingLLM and (2)
easy integration with existing attention optimization techniques.