Summary of Expansion Span: Combining Fading Memory and Retrieval in Hybrid State Space Models, by Elvis Nunez et al.
Expansion Span: Combining Fading Memory and Retrieval in Hybrid State Space Models
by Elvis Nunez, Luca Zancato, Benjamin Bowman, Aditya Golatkar, Wei Xia, Stefano Soatto
First submitted to arxiv on: 17 Dec 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 Medium Difficulty summary: The paper introduces Span-Expanded Attention (SE-Attn), a method that combines State Space Models (SSMs) and Attention mechanisms. Unlike current hybrid architectures, SE-Attn allows the state to be allocated based on relevancy rather than recency, enabling the model to access tokens from beyond its attention span without requiring extra hardware resources. The paper proposes a novel fine-tuning method, HyLoRA, which extends LoRA to hybrid models and enables efficient adaptation on long sequences of tokens. Experimental results show that SE-Attn enables pre-trained hybrid models to be adapted on natural language benchmarks with long-range dependencies, such as PG-19 and RULER, more efficiently and accurately than alternatives like LongLoRA. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This paper is about making computers better at understanding long sentences. Right now, there are limitations in how well they can understand these kinds of texts. The authors introduce a new way to make models remember things from the past, not just what’s happening right now. This allows them to understand longer sentences and do tasks like summarizing or translating text more accurately. |
Keywords
» Artificial intelligence » Attention » Fine tuning » Lora
