no code implementations • 7 Aug 2023 • David N Palacio, Alejandro Velasco, Daniel Rodriguez-Cardenas, Kevin Moran, Denys Poshyvanyk
To this end, this paper introduces ASTxplainer, an explainability method specific to LLMs for code that enables both new methods for LLM evaluation and visualizations of LLM predictions that aid end-users in understanding model predictions.
no code implementations • 7 Feb 2023 • David N. Palacio, Alejandro Velasco, Nathan Cooper, Alvaro Rodriguez, Kevin Moran, Denys Poshyvanyk
To demonstrate the practical benefit of $do_{code}$, we illustrate the insights that our framework can provide by performing a case study on two popular deep learning architectures and ten NCMs.