Summary of Can Transformers Learn to Solve Problems Recursively?, by Shizhuo Dylan Zhang et al.
Can Transformers Learn to Solve Problems Recursively?
by Shizhuo Dylan Zhang, Curt Tigges, Stella Biderman, Maxim Raginsky, Talia Ringer
First submitted to arxiv on: 24 May 2023
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 Neural networks have shown promise in software engineering and formal verification, but their ability to model semantic information remains unclear. This paper investigates the behavior of neural networks learning algorithms relevant to programs and formal verification proofs through mechanistic interpretability. We focus on structural recursion, a crucial aspect of tasks where symbolic tools outperform neural models. Our evaluation assesses transformer models’ ability to learn structurally recursive functions from input-output examples. We analyze the limitations and capabilities of transformer models in approximating these functions, as well as reconstructing the “shortcut” algorithms they learn. Our results show that we can correctly predict 91 percent of failure cases for one of the approximated functions. This work provides a new foundation for understanding neural networks’ behavior when they fail to solve tasks they’re trained for. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how well neural networks can understand and write software code, as well as formally verify it. It’s not clear if popular neural architectures like transformers are good at modeling the meaning behind this code. The researchers looked specifically at a type of recursion that’s important in computer science. They tested transformer models to see how well they could learn to write code that uses this type of recursion. The results show that these models can correctly predict when they’ll fail to solve certain tasks, which is an important step forward in understanding how neural networks work. |
Keywords
» Artificial intelligence » Transformer
