FiLM-Coordinated Dual-Branch Transformer for Global-Local Dependency Modeling in Language Modeling
Standard Transformers use a single self-attention pathway to model both global dependencies and local patterns, creating tension between long-range structural reasoning and fine-grained local representation learning. We propose a FiLM-coordinated dual-branch Transformer for language modeling, where each layer explicitly contains a global branch and a local branch, and feature-wise linear modulation (FiLM) is used for dynamic cross-branch coordination instead of simple concatenation or static addition. The key idea is that the two branches represent different dependency views of the same input, making channel-wise calibration more suitable than heavy token-level interaction. We therefore d...
| Authors | Zhiqiang Zhou, Xu Ling, Junliang Dai |
|---|---|
| Primary category | cs.CL |
| Published | 19 June 2026 |
| Last updated at source | 19 June 2026 |
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