Summary of Configurable Foundation Models: Building Llms From a Modular Perspective, by Chaojun Xiao et al.
Configurable Foundation Models: Building LLMs from a Modular Perspective
by Chaojun Xiao, Zhengyan Zhang, Chenyang Song, Dazhi Jiang, Feng Yao, Xu Han, Xiaozhi Wang, Shuo Wang, Yufei Huang, Guanyu Lin, Yingfa Chen, Weilin Zhao, Yuge Tu, Zexuan Zhong, Ao Zhang, Chenglei Si, Khai Hao Moo, Chenyang Zhao, Huimin Chen, Yankai Lin, Zhiyuan Liu, Jingbo Shang, Maosong Sun
First submitted to arxiv on: 4 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); 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 This paper explores the challenges faced by Large Language Models (LLMs) in being efficiently computationally scalable due to their large parameters, making it difficult for them to be applied in scenarios where resources are limited. Inspired by modular thinking in the human brain, researchers have begun decomposing LLMs into functional modules that can be dynamically assembled to tackle complex tasks. This paper introduces the concept of “bricks” – modular units that can be combined to form larger models. The authors formalize these bricks as emergent bricks, which emerge during pre-training, and customized bricks, which are constructed post-training to improve model capabilities. They also present four brick-oriented operations: retrieval and routing, merging, updating, and growing. These operations enable dynamic configuration of LLMs based on instructions. An empirical analysis is conducted on widely-used LLMs, revealing that FFN layers exhibit modular patterns with functional specialization of neurons. The paper concludes by highlighting open issues and directions for future research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how Large Language Models (LLMs) can be more efficient and effective. Right now, these models are really big and need a lot of computer power to work well. But what if we could break them down into smaller parts that could work together in different ways? That’s basically what this paper is all about – it explores the idea of taking LLMs apart into “bricks” that can be combined to do different tasks. The authors show how these bricks can be used to make new models that are better at certain things, and they even give examples of how this works in practice. |
