Summary of How Far Can Transformers Reason? the Globality Barrier and Inductive Scratchpad, by Emmanuel Abbe et al.
How Far Can Transformers Reason? The Globality Barrier and Inductive Scratchpad
by Emmanuel Abbe, Samy Bengio, Aryo Lotfi, Colin Sandon, Omid Saremi
First submitted to arxiv on: 10 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
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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 investigates the capabilities of Transformers in learning novel syllogisms by combining existing ones. The authors introduce the concept of “globality degree” to quantify when weak learning is feasible with regular Transformers. They show that distributions with high globality cannot be efficiently learned, and that long chains of syllogisms cannot be composed. The authors also develop scratchpad techniques, including agnostic and educated approaches, which can break the globality barrier but not necessarily generalize out-of-distribution (OOD). A novel “inductive scratchpad” is proposed to compose prior information more efficiently, achieving length generalizations up to 6x for arithmetic tasks depending on input formatting. The paper’s findings have implications for the expressivity and learnability of Transformers in various domains. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research explores how powerful AI models called Transformers can learn new logical rules by combining simpler ones. The authors want to know what kind of problems these models can solve from scratch, without being taught beforehand. They introduce a new way to measure the difficulty of learning new rules, and show that some types of rules are too hard for the models to learn efficiently. The researchers also develop new techniques to help the models learn better, including combining their own ideas with prior knowledge. This work has implications for how we use AI in various fields. |
