| Challenge: | Recent advances in Large Language Models (LLMs) are known for their computational and storage requirements due to the quadratic computation complexity of softmax attention. |
| Approach: | They propose to reduce the quadratic computation complexity of softmax attention by using feature maps, normalization and the gating mechanism to improve performance. |
| Outcome: | The proposed model outperforms existing gated linear attention models in extensive tasks including training from scratch and post-linearization with continual pre-training. |
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Chien Van Nguyen, Huy Huu Nguyen, Ruiyi Zhang, Hanieh Deilamsalehy, Puneet Mathur, Viet Dac Lai, Haoliang Wang, Jayakumar Subramanian, Ryan A. Rossi, Trung Bui, Nikos Vlassis, Franck Dernoncourt, Thien Huu Nguyen
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Modeling Context With Linear Attention for Scalable Document-Level Translation (2022.findings-emnlp)
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| Challenge: | Document-level machine translation models lack quadratic complexity in the sequence length due to their attention layers. |
| Approach: | They evaluate a recent linear attention model with a sentential gate to promote a recency inductive bias and compare it to open-source document translation. |
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ABC: Attention with Bounded-memory Control (2022.acl-long)
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Hao Peng, Jungo Kasai, Nikolaos Pappas, Dani Yogatama, Zhaofeng Wu, Lingpeng Kong, Roy Schwartz, Noah A. Smith
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Sparse Attention with Linear Units (2021.emnlp-main)
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| Challenge: | Recent studies have suggested that sparse attention mechanisms can be made more interpretable by replacing the softmax activation with its sparser variants. |
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Analyzing Memorization in Large Language Models through the Lens of Model Attribution (2025.naacl-long)
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| Challenge: | Existing research has focused on extracting memorized content from LLMs or developing memorization metrics without exploring the underlying architectural factors that contribute to memorizing. |
| Approach: | They analyze how attention modules at different layers impact its memorization and generalization performance by using attribution techniques. |
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The Devil in Linear Transformer (2022.emnlp-main)
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| Challenge: | Existing linear transformers suffer from performance degradations on various tasks and corpus. |
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IAM: Efficient Inference through Attention Mapping between Different-scale LLMs (2025.acl-long)
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| Challenge: | Large language models (LLMs) are a challenge due to their internal reasoning processes. |
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| Challenge: | Large language models struggle when dealing with complex, ill-formed, or noisy inputs . open-source models are less robust, while closed-source ones are more robust . |
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Chain and Causal Attention for Efficient Entity Tracking (2024.emnlp-main)
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| Challenge: | Existing approaches to handle entity tracking require at least log2 (n+1) layers to handle n state changes. |
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Scalable Efficient Training of Large Language Models with Low-dimensional Projected Attention (2024.emnlp-main)
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| Challenge: | Existing studies have found that low-rank pre-training often compromises effectiveness. |
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