Summary of A Percolation Model Of Emergence: Analyzing Transformers Trained on a Formal Language, by Ekdeep Singh Lubana et al.
A Percolation Model of Emergence: Analyzing Transformers Trained on a Formal Language
by Ekdeep Singh Lubana, Kyogo Kawaguchi, Robert P. Dick, Hidenori Tanaka
First submitted to arxiv on: 22 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 delves into the phenomenon of “emergence” in neural networks, where an increase in data, size, or compute can suddenly lead to specific capabilities. To establish causal factors and enable risk regulation frameworks for AI, researchers must understand the underlying causes of emergent capabilities. Inspired by the study of emergent properties in other fields, this work proposes a phenomenological definition for emergence in neural networks, linking it to the acquisition of general structures underlying data-generating processes. Experimental results using Transformers trained on context-sensitive formal languages show that once underlying grammar and context-sensitivity inducing structures are learned, performance suddenly improves on narrower tasks. This phenomenon is analogous to percolation on bipartite graphs, predicting a phase transition point that shifts with changing data structure. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how artificial intelligence (AI) can suddenly learn new things when given more information or computing power. Researchers want to understand why this happens so they can make sure AI systems don’t get too powerful and cause harm. The authors looked at other areas where “emergent properties” appear, like physics and biology, and used those ideas to define what’s happening in neural networks. They tested their idea with a special kind of AI called Transformers, which learned to recognize patterns in language and suddenly got better at doing specific tasks. This is similar to how some materials change from solid to liquid when heated, and the authors think this could help us predict when AI will start learning new things. |
