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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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GrooveSquid.com Paper Summaries

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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