Transformer-based Program Synthesis for Low-Data Environments. (arXiv:2205.09246v1 [cs.PL])

Recent advancements in large pre-trained transformer models (GPT2/3, T5) have
found use in program synthesis to generate programs that satisfy a set of
input/output examples. However, these models perform poorly on long-horizon and
low-data tasks, and often don't seem to understand the semantics of the
languages they generate. We investigate an approach that tackles both of these
issues, by using attributed context-free-grammars of programming languages to
generate programs, and then analyzing generated programs so that they can be
annotated with …

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