4 citations · 14 across the 15 of their papers we have counts for
3 papers · 1 filter
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…
Towards More Trustworthy and Interpretable LLMs for Code through Syntax-Grounded Explanations
David N. Palacio, Daniel Rodriguez-Cardenas, Alejandro Velasco +3
Trustworthiness and interpretability are inextricably linked concepts for LLMs. The more interpretable an LLM is, the more trustworthy it becomes. However, current techniques for i…
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…