Passive Learning of Lattice Automata from Recurrent Neural Networks
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | |
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| _version_ | 1866914320067067904 |
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| author | Slimi, Jaouhar Gall, Tristan Le Lemesle, Augustin |
| author_facet | Slimi, Jaouhar Gall, Tristan Le Lemesle, Augustin |
| contents | We present a passive automata learning algorithm that can extract automata from recurrent networks with very large or even infinite alphabets. Our method combines overapproximations from the field of Abstract Interpretation and passive automata learning from the field of Grammatical Inference. We evaluate our algorithm by first comparing it with the state-of-the-art automata extraction algorithm from Recurrent Neural Networks trained on Tomita grammars. Then, we extend these experiments to regular languages with infinite alphabets, which we propose as a novel benchmark. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_22489 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Passive Learning of Lattice Automata from Recurrent Neural Networks Slimi, Jaouhar Gall, Tristan Le Lemesle, Augustin Formal Languages and Automata Theory We present a passive automata learning algorithm that can extract automata from recurrent networks with very large or even infinite alphabets. Our method combines overapproximations from the field of Abstract Interpretation and passive automata learning from the field of Grammatical Inference. We evaluate our algorithm by first comparing it with the state-of-the-art automata extraction algorithm from Recurrent Neural Networks trained on Tomita grammars. Then, we extend these experiments to regular languages with infinite alphabets, which we propose as a novel benchmark. |
| title | Passive Learning of Lattice Automata from Recurrent Neural Networks |
| topic | Formal Languages and Automata Theory |
| url | https://arxiv.org/abs/2509.22489 |