4 citations · 8 across the 4 of their papers we have counts for
4 papers
Measuring Emergent Capabilities of LLMs for Software Engineering: How Far Are We?
Conor O'Brien, Daniel Rodriguez-Cardenas, Alejandro Velasco +2
The adoption of Large Language Models (LLMs) across multiple contexts has sparked interest in understanding how scaling model size might lead to behavioral changes, as LLMs can exh…
Which Syntactic Capabilities Are Statistically Learned by Masked Language Models for Code?
Alejandro Velasco, David N. Palacio, Daniel Rodriguez-Cardenas +1
This paper discusses the limitations of evaluating Masked Language Models (MLMs) in code completion tasks. We highlight that relying on accuracy-based measurements may lead to an o…
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…