Exploring and Evaluating Personalized Models for Code Generation. (arXiv:2208.13928v1 [cs.SE])

Large Transformer models achieved the state-of-the-art status for Natural
Language Understanding tasks and are increasingly becoming the baseline model
architecture for modeling source code. Transformers are usually pre-trained on
large unsupervised corpora, learning token representations and transformations
relevant to modeling generally available text, and are then fine-tuned on a
particular downstream task of interest. While fine-tuning is a tried-and-true
method for adapting a model to a new domain -- for example, question-answering
on a given topic -- generalization remains an …

arxiv code code generation generation personalized