45 citations · 79 across the 19 of their papers we have counts for
3 papers · 1 filter
Benchmarking Causal Study to Interpret Large Language Models for Source Code
Daniel Rodriguez-Cardenas, David N. Palacio, Dipin Khati +2
One of the most common solutions adopted by software researchers to address code generation is by training Large Language Models (LLMs) on massive amounts of source code. Although…
Evaluating and Explaining Large Language Models for Code Using Syntactic Structures
David N Palacio, Alejandro Velasco, Daniel Rodriguez-Cardenas +2
Large Language Models (LLMs) for code are a family of high-parameter, transformer-based neural networks pre-trained on massive datasets of both natural and programming languages. T…
Toward a Theory of Causation for Interpreting Neural Code Models
David N. Palacio, Alejandro Velasco, Nathan Cooper +3
Neural Language Models of Code, or Neural Code Models (NCMs), are rapidly progressing from research prototypes to commercial developer tools. As such, understanding the capabilitie…