Papers with vanilla self-attention
Multimodal Graph Transformer for Multimodal Question Answering (2023.eacl-main)
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| Challenge: | a myriad of complex tasks require both prior knowledge and reasoning intelligence. |
| Approach: | They propose a plug-and-play quasi-attention mechanism to integrate multimodal graph information to vanilla self-attention as effective prior. |
| Outcome: | The proposed model is able to perform reasoning across multiple modalities. |
Integral Transformer: Denoising Attention, Not Too Much Not Too Little (2025.emnlp-main)
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| Challenge: | Existing methods to reduce attention noise by integrating signals from logit distributions are prone to attention noise. |
| Approach: | They propose a self-attention mechanism that integrates signals from the logit distribution to denoise attention. |
| Outcome: | The proposed model outperforms vanilla, Cog, and Differential attention variants on knowledge and reasoning benchmarks. |
CoCA: Fusing Position Embedding with Collinear Constrained Attention in Transformers for Long Context Window Extending (2024.acl-long)
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| Challenge: | Existing models that use self-attention and position embedding have anomalous behavior that hinder long context window extrapolation. |
| Approach: | They propose a collinear constraint between Q and K to integrate RoPE and self-attention. |
| Outcome: | The proposed model integrates self-attention and position embedding into LLMs without fine-tuning. |