Summary of Transformers Meet Neural Algorithmic Reasoners, by Wilfried Bounsi et al.
Transformers meet Neural Algorithmic Reasoners
by Wilfried Bounsi, Borja Ibarz, Andrew Dudzik, Jessica B. Hamrick, Larisa Markeeva, Alex Vitvitskyi, Razvan Pascanu, Petar Veličković
First submitted to arxiv on: 13 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: 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 Transformers have revolutionized machine learning with their simple yet effective architecture. A novel approach combines the Transformer’s language understanding with graph neural network (GNN)-based neural algorithmic reasoners (NARs) to address limitations in natural language understanding (NLU) tasks. The proposed hybrid architecture, TransNAR, allows tokens in the language model to cross-attend to node embeddings from the NAR, achieving significant gains over Transformer-only models for algorithmic reasoning on CLRS-Text and demonstrating robustness both in and out of distribution. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Transformers are super smart machines that can understand lots of different languages. They’re great at understanding natural language, like human speech. But they’re not as good at doing math problems or following rules. To help them do those things better, researchers created a new way to combine the Transformer’s language skills with another type of AI called graph neural networks (GNNs). The GNNs are good at doing math and following rules, so when you put them together, it makes the Transformer much stronger. This new AI system is called TransNAR. It’s like a superhero that can understand languages and do math problems! |
Keywords
» Artificial intelligence » Gnn » Graph neural network » Language model » Language understanding » Machine learning » Transformer
