Summary of Samba: Simple Hybrid State Space Models For Efficient Unlimited Context Language Modeling, by Liliang Ren et al.
Samba: Simple Hybrid State Space Models for Efficient Unlimited Context Language Modeling
by Liliang Ren, Yang Liu, Yadong Lu, Yelong Shen, Chen Liang, Weizhu Chen
First submitted to arxiv on: 11 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Samba, a novel hybrid architecture, efficiently models sequences with infinite context length by combining Mamba’s selective State Space Model (SSM) with Sliding Window Attention (SWA). This approach selectively compresses sequences into recurrent hidden states while maintaining precise recent memory recall. Samba outperforms state-of-the-art models across various benchmarks, including zero-shot and finetuned settings. When pre-trained on 4K-length sequences, Samba extrapolates to 1M context lengths with perfect memory recall on the Passkey Retrieval task. Our code for training on open-source data is publicly available at this https URL. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper introduces a new way to understand and analyze long sequences of text. The method, called Samba, is better than existing approaches at handling sequences that are hundreds of times longer. Samba uses a combination of two techniques: compressing the sequence into a shorter form, while still keeping track of recent information. This allows Samba to accurately remember and recall details from earlier in the sequence. The paper shows that Samba can handle long sequences with ease and even improve its performance when given more data. |
Keywords
» Artificial intelligence » Attention » Context length » Recall » Zero shot
