Summary of Pretrained Hybrids with Mad Skills, by Nicholas Roberts et al.
Pretrained Hybrids with MAD Skills
by Nicholas Roberts, Samuel Guo, Zhiqi Gao, Satya Sai Srinath Namburi GNVV, Sonia Cromp, Chengjun Wu, Chengyu Duan, Frederic Sala
First submitted to arxiv on: 2 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 proposes a framework called Manticore that automates the design of hybrid large language model architectures. Currently, choosing the right LM architecture is challenging due to the variety of alternative architectures with unique capabilities and tradeoffs. Hybrid designs require manual expert-driven search and retraining from scratch. Manticore addresses these challenges by incorporating simple projectors that translate features between pretrained blocks from different architectures, allowing for the creation of pretrained hybrids. The framework enables LM selection without training multiple models, construction of pretrained hybrids from existing models, and programming of hybrids to have specific capabilities. Manticore’s approach outperforms manually-designed hybrids on Long Range Arena tasks, achieving strong performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about creating a new way to design special kinds of computer programs called large language models. These programs are important for many applications like answering questions and generating text. There are many different ways to design these programs, but choosing the right one can be tricky. The authors propose a new approach that uses ideas from previous work to create a framework called Manticore. Manticore allows you to combine the strengths of different program designs without having to start from scratch or train multiple models. This means you can make more accurate predictions and improve the performance of these programs. |
Keywords
» Artificial intelligence » Large language model
