Cluster-Former: Clustering-based Sparse Transformer for Question Answering (2021.findings-acl)
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| Challenge: | Existing models for encoding long sequences in deep learning suffer from high latency and memory demands. |
| Approach: | They propose a clustering-based sparse Transformer framework to perform attention across chunked sequences. |
| Outcome: | The proposed framework achieves state-of-the-art on several major QA benchmarks. |
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| Challenge: | Recent studies show that Transformers can process longer sequences because of their complexity and time scales quadratic to the sequence length. |
| Approach: | They propose an efficient Transformer model with adaptive attention that can select useful tokens automatically in sparse attention by learnable position vectors. |
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ClusterFormer: Neural Clustering Attention for Efficient and Effective Transformer (2022.acl-long)
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| Challenge: | Existing sparse attention methods use fixed patterns to select words without considering similarities between words. |
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RealFormer: Transformer Likes Residual Attention (2021.findings-acl)
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| Challenge: | Existing techniques to create Residual Attention Layer Transformer networks outperform the canonical Transformer on a wide spectrum of tasks. |
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Efficient Long-Range Transformers: You Need to Attend More, but Not Necessarily at Every Layer (2023.findings-emnlp)
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Efficient Content-Based Sparse Attention with Routing Transformers (2021.tacl-1)
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| Challenge: | Self-attention suffers from quadratic computation and memory requirements with respect to sequence length . despite its effectiveness, self-attention models suffer from quadratic computation and a limited set of locations . |
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| Challenge: | Large pretrained Transformer models have proven capable at tackling natural language tasks, but handling long sequence inputs still poses a significant challenge. |
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| Challenge: | Recent literature in text classification is biased towards short text sequences . multi-page multi-paragraph documents cannot be efficiently encoded by vanilla transformers based on short text. |
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ChuLo: Chunk-Level Key Information Representation for Long Document Understanding (2025.findings-acl)
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| Challenge: | Traditional approaches to truncate inputs, sparse self-attention, and chunking often lead to information loss and hinder the model’s ability to capture long-range dependencies. |
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