Summary of Understanding Emergent Abilities Of Language Models From the Loss Perspective, by Zhengxiao Du et al.
Understanding Emergent Abilities of Language Models from the Loss Perspective
by Zhengxiao Du, Aohan Zeng, Yuxiao Dong, Jie Tang
First submitted to arxiv on: 23 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 study challenges the notion that large language models exclusively possess emergent abilities. It proposes a new perspective on these abilities by focusing on pre-training loss instead of model size or training compute. The researchers demonstrate that Transformer models with similar pre-training losses, but varying model and data sizes, achieve identical performance on downstream tasks. They also discover that a model exhibits emergent abilities when its pre-training loss falls below a specific threshold, regardless of metric continuity. This finding inspires the redefinition of emergent abilities as those that occur in models with lower pre-training losses. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study is about understanding how language models work. It used to be thought that only big models could do clever things, but now we know that smaller models can do similar things too! The researchers looked at how well these models did on different tasks and found that it’s not just about the size of the model or how much data it was trained on. Instead, they found that what matters is how “good” the model was during its training. |
Keywords
* Artificial intelligence * Transformer
