Summary of Rethinking Optimization and Architecture For Tiny Language Models, by Yehui Tang et al.
Rethinking Optimization and Architecture for Tiny Language Models
by Yehui Tang, Fangcheng Liu, Yunsheng Ni, Yuchuan Tian, Zheyuan Bai, Yi-Qi Hu, Sichao Liu, Shangling Jui, Kai Han, Yunhe Wang
First submitted to arxiv on: 5 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 The paper explores the challenges of applying large language models (LLMs) on mobile devices, which require tiny language models with high performance. To address this issue, the authors design a series of empirical studies to analyze the effects of each component in optimizing LLMs. They focus on three perspectives: neural architecture, parameter initialization, and optimization strategy. The study finds several design formulas empirically effective for tiny LLMs, including tokenizer compression, architecture tweaking, parameter inheritance, and multiple-round training. Experimental results show that optimized models PanGu-π-1B Pro and PanGu-π-1.5B Pro achieve significant improvements on benchmark evaluation sets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making language models work better on mobile devices. Right now, these models are too big and use too much computing power to run on phones. The researchers want to make smaller language models that can still do a good job. They studied different ways to optimize language model performance, like choosing the right neural network architecture or initializing model parameters well. They found some formulas work better than others for making tiny language models. They tested their ideas and found that they could improve language model performance by 8.87%. This means the new models are better than older ones at doing things like understanding text. |
Keywords
* Artificial intelligence * Language model * Neural network * Optimization * Tokenizer
