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Evaluating and Explaining Large Language Models for Code Using Syntactic Structures
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.
Toward a Theory of Causation for Interpreting Neural Code Models
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.
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