Grokking in the Wild: Data Augmentation for Real-World Multi-Hop Reasoning with Transformers
Roman Abramov, Felix Steinbauer, Gjergji Kasneci
2025-05-06
Summary
This paper talks about how using extra, computer-generated data can help AI models get much better at solving tough problems that require several steps of reasoning, especially when dealing with real-world information.
What's the problem?
AI models often have trouble figuring out answers that need them to connect multiple facts or steps, especially when the information comes from complicated sources like knowledge graphs.
What's the solution?
The researchers improved the AI's reasoning skills by creating more practice data using synthetic data augmentation, which helps the models learn how to handle multi-step questions and find the right answers more often.
Why it matters?
This matters because it means AI can become much smarter at understanding and solving real-world problems, making it more useful for research, education, and any situation where deep reasoning is needed.
Abstract
Grokking with synthetic data augmentation in knowledge graphs enhances Transformer models' multi-step factual reasoning on real-world tasks, achieving high accuracy on benchmarks.