14 citations · 102 across the 23 of their papers we have counts for
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cs.PL2022★ 9 cited
NatGen: Generative pre-training by "Naturalizing" source code
Saikat Chakraborty, Toufique Ahmed, Yangruibo Ding +2
Pre-trained Generative Language models (e.g. PLBART, CodeT5, SPT-Code) for source code yielded strong results on several tasks in the past few years, including code generation and…
cs.SE2022★ 10 cited
Few-shot training LLMs for project-specific code-summarization
Toufique Ahmed, Premkumar Devanbu
Very large language models (LLMs), such as GPT-3 and Codex have achieved state-of-the-art performance on several natural-language tasks, and show great promise also for code. A par…
cs.SE2022★ 6 cited
Learning code summarization from a small and local dataset
Toufique Ahmed, Premkumar Devanbu
Foundation models (e.g., CodeBERT, GraphCodeBERT, CodeT5) work well for many software engineering tasks. These models are pre-trained (using self-supervision) with billions of code…