Summary of Otce: Hybrid Ssm and Attention with Cross Domain Mixture Of Experts to Construct Observer-thinker-conceiver-expresser, by Jingze Shi et al.

OTCE: Hybrid SSM and Attention with Cross Domain Mixture of Experts to construct Observer-Thinker-Conceiver-Expresser

by Jingze Shi, Ting Xie, Bingheng Wu, Chunjun Zheng, Kai Wang

First submitted to arxiv on: 24 Jun 2024

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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 The proposed paper combines Mamba and Transformer architectures to improve language modeling tasks. The combination leverages the strengths of both architectures, including the quadratic self-attention mechanism that alleviates shortcomings in handling long-term dependencies. A position information injection method integrates the two architectures with hybrid experts, enabling advantages from both. The paper also presents a new architecture, Observer-Thinker-Conceiver-Expresser (OTCE), which competes with medium-scale language models on a small scale.
Low	GrooveSquid.com (original content)	Low Difficulty Summary The proposed paper combines Mamba and Transformer architectures to improve language modeling tasks. This means that the researchers took two existing ideas and mixed them together to make something even better! The combination helps with things like remembering long words in a sentence, and makes it possible for computers to understand natural language more easily. The new architecture is called OTCE, which is kind of like how we think – first, we observe something, then we think about it, and finally, we express our thoughts.