Summary of Transformer-based Models Are Not Yet Perfect at Learning to Emulate Structural Recursion, by Dylan Zhang et al.
Transformer-Based Models Are Not Yet Perfect At Learning to Emulate Structural Recursion
by Dylan Zhang, Curt Tigges, Zory Zhang, Stella Biderman, Maxim Raginsky, Talia Ringer
First submitted to arxiv on: 23 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Formal Languages and Automata Theory (cs.FL); Logic in Computer Science (cs.LO); Programming Languages (cs.PL)
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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 explores the capabilities of transformer-based models in learning structural recursion from examples. Recursion, a fundamental concept in both natural and formal languages, is crucial for tasks like programming language processing and formal mathematics, where symbolic tools currently outperform neural networks. The study introduces a general framework that connects abstract concepts of structural recursion to concrete sequence modeling problems and learned models’ behavior. This framework includes a representation capturing the syntax of structural recursion, along with two frameworks explaining their semantics: one rooted in programming languages and another bridging this perspective with the transformer architecture. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how AI models can learn about recursive ideas from examples. Recursion is important because it helps us understand complex patterns in language. The study shows that transformer-based models, which are good at understanding sequences of words, can also learn to recognize recursive structures. This has implications for tasks like programming and formal mathematics. |
Keywords
» Artificial intelligence » Semantics » Syntax » Transformer
