SparseFlow: Accelerating Transformers by Sparsifying Information Flows (2024.acl-long)
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| Challenge: | SparseFlow is an efficient method to sparsify the dense information flows within transformers. |
| Approach: | They propose a method to sparsify the dense pathways of token representations across all transformer blocks by parameterizing them to be sparse. |
| Outcome: | The proposed method reduces computational costs by half on average without compromising task accuracy. |
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| Challenge: | Existing approaches to sparsify attention in the Transformer model are based on quadratic memory complexity and a lack of information for each word. |
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Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Remi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, Alexander Rush
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| Challenge: | Recent advances in transformer quantization have shown remarkable improvement in many Natural Language Processing tasks and beyond. |
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Tianchu Ji, Shraddhan Jain, Michael Ferdman, Peter Milder, H. Andrew Schwartz, Niranjan Balasubramanian
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Yao Chen, Yilong Chen, Yinqi Yang, Junyuan Shang, Zhenyu Zhang, Zefeng Zhang, Shuaiyi Nie, Shuohuan Wang, Yu Sun, Hua Wu, Haifeng Wang, Tingwen Liu
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| Challenge: | Existing Transformer-based LLMs have limited performance due to complexity of attention module . key-value cache is the major memory footprint and inference latency problem . |
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VISIT: Visualizing and Interpreting the Semantic Information Flow of Transformers (2023.findings-emnlp)
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| Challenge: | Recent work in interpretability suggests we can project weights and hidden states of transformer-based language models (LMs) to their vocabulary space, a transformation that makes them more human interpretable. |
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