Summary of Densemamba: State Space Models with Dense Hidden Connection For Efficient Large Language Models, by Wei He et al.
DenseMamba: State Space Models with Dense Hidden Connection for Efficient Large Language Models
by Wei He, Kai Han, Yehui Tang, Chengcheng Wang, Yujie Yang, Tianyu Guo, Yunhe Wang
First submitted to arxiv on: 26 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces DenseSSM, a novel approach to enhance the flow of hidden information between layers in state space models (SSMs). By selectively integrating shallow-layer hidden states into deeper layers, DenseSSM retains fine-grained information crucial for the final output. The method is designed to retain training parallelizability and inference efficiency while achieving significant improvements over original SSMs. Dense connections are applied to various SSM types, including RetNet and Mamba, resulting in up to 5% accuracy improvement on public benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper develops a new type of foundational network architecture that is more efficient than the commonly used Transformer architecture. It’s called DenseSSM, and it helps state space models learn better by keeping important information flowing between layers. This makes it useful for tasks like image recognition and natural language processing. The method is easy to train and use, and it even works with other types of models. |
Keywords
* Artificial intelligence * Inference * Natural language processing * Transformer
