FiLM-Coordinated Dual-Branch Transformer for Global-Local Dependency Modeling in Language Modeling

• URL: http://arxiv.org/abs/2606.21075v1
• Date: Fri, 19 Jun 2026 03:48:49 GMT
• Title: FiLM-Coordinated Dual-Branch Transformer for Global-Local Dependency Modeling in Language Modeling
• Abstract summary: We propose a FiLM-coordinated dual-branch Transformer for language modeling.<n>The two branches represent different dependency views of the same input.<n>Experiments on small-scale language modeling settings show that the proposed structure consistently outperforms same-width single-branch baselines.
• Score: 4.131782714245991
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 design a bidirectional FiLM module in which each branch generates per-channel scaling and shifting parameters to condition the other. Experiments on multiple small-scale language modeling settings show that the proposed structure consistently outperforms same-width single-branch baselines and weakened dual-branch variants under a fixed lightweight configuration. On TinyShakespeare and a 1M-character subset of WikiText-2, the full dual-branch FiLM model achieves the best results among same-width structural baselines. Multi-seed results support the stability of the gains, while mechanistic analyses show that FiLM learns input-dependent, layer-dependent, and channel-selective modulation patterns rather than static scaling. Parameter-matched widened single-branch baselines also indicate that the current design still leaves room for improvement in parameter efficiency.

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