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Towards More Trustworthy and Interpretable LLMs for Code through Syntax-Grounded Explanations

Fuente: arXiv

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Dettagli Bibliografici
Autori principali:	Palacio, David N., Rodriguez-Cardenas, Daniel, Velasco, Alejandro, Khati, Dipin, Moran, Kevin, Poshyvanyk, Denys
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Software Engineering Artificial Intelligence Machine Learning
Accesso online:	Acceder al recurso
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