Summary of Extracting Finite State Machines From Transformers, by Rik Adriaensen et al.
Extracting Finite State Machines from Transformers
by Rik Adriaensen, Jaron Maene
First submitted to arxiv on: 8 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 The abstract explores the trainability of transformer architectures on regular languages, building upon their popularity in deep learning. The study seeks to provide a fine-grained understanding of transformers’ capabilities by investigating them from a mechanistic interpretability perspective. Using an extension of the L* algorithm, researchers extract Moore machines from transformers and empirically determine tighter lower bounds on their trainability when a finite number of symbols determine the state. Additionally, they characterise the regular languages a one-layer transformer can learn with good length generalisation. However, the study also identifies failure cases where misrecognition occurs due to saturation of the attention mechanism. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Transformers are powerful tools in deep learning, but what can they do with regular languages? Researchers wanted to find out how well transformers could learn from and understand regular languages. They used a special algorithm to figure out how transformers worked and found that they could learn certain patterns and rules. But sometimes, the transformers got stuck or misinterpreted the information because of how they processed it. |
Keywords
» Artificial intelligence » Attention » Deep learning » Transformer
