Papers › Cluster-Former: Clustering-based Sparse Transformer for Long-Range Dependency Encoding
Cluster-Former: Clustering-based Sparse Transformer for Long-Range Dependency Encoding
Shuohang Wang, Luowei Zhou, Zhe Gan, Yen-Chun Chen, Yuwei Fang, Siqi Sun, Yu Cheng, Jingjing Liu
Transformer has become ubiquitous in the deep learning field. One of the key ingredients that destined its success is the self-attention mechanism, which allows fully-connected contextual encoding over input tokens. However, despite its effectiveness in modeling short sequences, self-attention suffers when handling inputs with extreme long-range dependencies, as its complexity grows quadratically with respect to the sequence length. Therefore, long sequences are often encoded by Transformer in chunks using a sliding window. In this paper, we propose Cluster-Former, a novel clustering-based sparse Transformer to perform attention across chunked sequences. The proposed framework is pivoted on two unique types of Transformer layer: Sliding-Window Layer and Cluster-Former Layer, which encode local sequence information and global context jointly and iteratively. This new design allows information integration beyond local windows, which is especially beneficial for question answering (QA) tasks that rely on long-range dependencies. Experiments show that Cluster-Former achieves state-of-the-art performance on several major QA benchmarks.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Language Modelling | enwik8 | Cluster-Former (#C=512) | Bit per Character (BPC) | 1.22 | #32 of 42 | Archive leaderboard | report |
| Open-Domain Question Answering | SearchQA | Cluster-Former (#C=512) | EM | 68.0 | #1 of 14 | Archive leaderboard | report |
| Question Answering | Natural Questions (long) | Cluster-Former (#C=512) | F1 | 76.5 | #2 of 13 | Archive leaderboard | report |
| Question Answering | Quasart-T | Cluster-Former (#C=512) | EM | 54 | #1 of 7 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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