Summary of Automata Extraction From Transformers, by Yihao Zhang et al.
Automata Extraction from Transformers
by Yihao Zhang, Zeming Wei, Meng Sun
First submitted to arxiv on: 8 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Formal Languages and Automata Theory (cs.FL)
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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 proposes a novel algorithm for extracting automata from Transformer-based machine learning models, building upon the success of automata extraction methods in understanding recurrent neural networks (RNNs). The proposed algorithm treats the Transformer model as a black-box system and tracks its internal latent representations during operation. By applying classical pedagogical approaches like the L* algorithm, the authors interpret the Transformer’s processing of formal languages as deterministic finite-state automata (DFA). This study not only enhances the interpretability of Transformer-based ML systems but also marks a crucial step toward understanding how ML systems process formal languages. The proposed algorithm is demonstrated on various datasets and tasks, showcasing its effectiveness in extracting meaningful insights from Transformer models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper explores ways to understand how machine learning (ML) models work. Specifically, it focuses on a type of model called the Transformer, which has been very successful in many applications. The problem is that we don’t really know how it makes decisions or processes information. To solve this mystery, the authors create a new algorithm that can extract information about how the Transformer model works. This helps us understand not only how the model makes predictions but also how it processes language. The study shows that this new approach can provide valuable insights into how ML models work and could lead to better performance in various applications. |
Keywords
» Artificial intelligence » Machine learning » Transformer
