Summary of Training Neural Networks As Recognizers Of Formal Languages, by Alexandra Butoi and Ghazal Khalighinejad and Anej Svete and Josef Valvoda and Ryan Cotterell and Brian Dusell
Training Neural Networks as Recognizers of Formal Languages
by Alexandra Butoi, Ghazal Khalighinejad, Anej Svete, Josef Valvoda, Ryan Cotterell, Brian DuSell
First submitted to arxiv on: 11 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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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 paper proposes a novel approach to evaluating the computational power of neural network architectures by training and testing them directly as binary classifiers of strings, rather than relying on proxy tasks. The authors correct a mismatch between formal language theory and existing experiments by applying an efficient length-controlled sampling algorithm for regular languages. They provide results on various languages across the Chomsky hierarchy using three neural architectures: RNN, LSTM, and causally-masked transformer. The findings suggest that RNNs and LSTMs often outperform transformers, and auxiliary training objectives can improve performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about fixing a problem in how we test how well artificial intelligence (AI) models work with language. Right now, we use proxy tasks that are not exactly like the theory of formal languages. The authors come up with a new way to train AI models directly as string classifiers, which will help us do more accurate testing. They also share some results on different languages and architectures, showing what works best. |
Keywords
» Artificial intelligence » Lstm » Neural network » Rnn » Transformer
