Lorsa is a novel attention decomposition method designed to tackle attention superposition, extracting tens of thousands of interpretable attention units from the attention layers of large language models.

The complete implementation of Lorsa has been migrated to the Language-Model-SAEs repository.

This repository is a comprehensive, fully-distributed framework for training, analyzing, and visualizing Sparse Autoencoders (SAEs) and their frontier variants, including:

• Lorsa (Low-Rank Sparse Attention)
• CLT (Cross-layer Transcoder)
• MoLT (Mixture of Linear Transforms)
• CrossCoder
• And many more SAE variants

• Code Implementation: OpenMOSS/Language-Model-SAEs
• Paper: Towards Understanding the Nature of Attention with Low-Rank Sparse Decomposition

Lorsa employs low-rank sparse decomposition to decompose attention layer outputs into interpretable feature units, effectively addressing the feature superposition problem in attention mechanisms. This enables a deeper understanding of how attention mechanisms work in large language models.

• Extract tens of thousands of interpretable attention units from attention layers
• Leverage low-rank sparse decomposition to handle attention superposition
• Support large-scale distributed training
• Provide comprehensive visualization tools

Please visit the Language-Model-SAEs repository for:

• Installation instructions
• Training examples
• Usage tutorials

If you use Lorsa in your research, please cite:

@article{He2025Lorsa,
  author    = {Zhengfu He and Junxuan Wang and Rui Lin and Xuyang Ge and 
               Wentao Shu and Qiong Tang and Junping Zhang and Xipeng Qiu},
  title     = {Towards Understanding the Nature of Attention with Low-Rank Sparse Decomposition},
  journal   = {CoRR},
  volume    = {abs/2504.20938},
  year      = {2025},
  url       = {https://arxiv.org/abs/2504.20938},
  eprint    = {2504.20938},
  eprinttype = {arXiv}
}

• 2025.4.29: Initial release of Lorsa, introducing low-rank sparse attention decomposition
• 2025.11.9: Implementation migrated to Language-Model-SAEs repository for better framework support

For any questions or suggestions, please submit an Issue in the Language-Model-SAEs repository!