Summary of A Complexity-based Theory Of Compositionality, by Eric Elmoznino et al.
A Complexity-Based Theory of Compositionality
by Eric Elmoznino, Thomas Jiralerspong, Yoshua Bengio, Guillaume Lajoie
First submitted to arxiv on: 18 Oct 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 proposed paper defines a formal concept called representational compositionality, which is believed to underlie intelligent behavior in both humans and AI systems. The authors argue that this concept can enable out-of-distribution generalization, allowing models to adapt to novel combinations of known concepts. They present a definition that consists of three properties: expressiveness, re-describability as a function of symbolic sequences with re-combinable parts, and simplicity in the relationship between these sequences and the representation. The authors validate their definition using synthetic and real-world data and show how it unifies disparate intuitions from across the literature. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new idea called representational compositionality, which is important for making AI smarter. Right now, there’s no clear way to measure this concept, but the researchers propose a definition that can be used with math. They suggest three things that make a representation compositional: it should be able to express lots of information, it should be possible to break down into smaller parts and reassemble them, and the relationship between these parts and the whole should be simple. The authors tested their idea using fake and real data and showed that it can help AI systems learn and adapt. |
Keywords
» Artificial intelligence » Generalization
