Summary of Simulating Weighted Automata Over Sequences and Trees with Transformers, by Michael Rizvi et al.
Simulating Weighted Automata over Sequences and Trees with Transformers
by Michael Rizvi, Maude Lizaire, Clara Lacroce, Guillaume Rabusseau
First submitted to arxiv on: 12 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computational Complexity (cs.CC)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 natural language processing (NLP) with impressive empirical successes. However, there is a lack of understanding about how they reason and their computational capabilities. Despite not processing data sequentially, transformers outperform sequential neural models like RNNs. Recent work has shown that transformers can simulate the sequential reasoning abilities of deterministic finite automata (DFAs). This study investigates whether transformers can simulate more complex finite state machines, such as weighted finite automata (WFAs) and weighted tree automata (WTA). The authors formally prove that transformers can simulate these models and provide upper bounds on the required model sizes. Empirically, synthetic experiments demonstrate that transformers can learn these compact solutions through standard gradient-based training. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Have you ever wondered how computers process information? Recently, a type of computer program called transformers has been doing really well at understanding language. But we don’t fully understand how they do it or what their limits are. These programs don’t work one step at a time like some others, but they still outperform those models. Researchers have found that transformers can mimic the way simple machines think, and now they’re trying to see if they can also mimic more complex machines. The study shows that transformers can indeed mimic these machines and even find ways to do it efficiently. This is important because it helps us understand how computers work and could lead to better language understanding in the future. |
Keywords
» Artificial intelligence » Language understanding » Natural language processing » Nlp
