Convolution for Large Language Models
Large language models (LLMs) largely rely on Transformers, where self-attention provides global token interaction but does not explicitly encode the locality of natural language. We study whether lightweight depthwise convolutions can supply this local inductive bias without materially increasing model size. Our macro-level ablation compares convolution at 17 locations in a Qwen3 Transformer block and finds the best results when convolution is applied to the projected queries, keys, and values before attention. A subsequent micro-level study favors a residual depthwise convolution with kernel size $k=3$, without additional normalization or activation. Across Qwen3 models and several pre-t...
| Authors | Yuchuan Tian, Yingte Shu, Wei He et al. |
|---|---|
| Primary category | cs.CL |
| Published | 20 July 2026 |
| Last updated at source | 20 July 2026 |
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