A Congruence-based Approach to Active Automata Learning from Neural Language Models
Resumen:
The paper proposes an approach for probably approximately correct active learning of probabilistic automata (PDFA) from neural language models. It is based on a congruence over strings which is parameterized by an equivalence relation over probability distribu- tions. The learning algorithm is implemented using a tree data structure of arbitrary (possibly unbounded) degree. The implementation is evaluated with several equivalences on LSTM and Transformer-based neural language models from different application do- mains.
| 2023 | |
| Agencia Nacional de Investigación e Innovación | |
|
Artificial intelligence Active learning Neural language models Ciencias Naturales y Exactas Ciencias de la Computación e Información Ciencias de la Computación |
|
| Inglés | |
| Agencia Nacional de Investigación e Innovación | |
| REDI | |
| https://hdl.handle.net/20.500.12381/3419 | |
| Acceso abierto | |
| Reconocimiento 4.0 Internacional. (CC BY) |
