ReGAL: Refactoring Programs to Discover Generalizable Abstractions. (arXiv:2401.16467v1 [cs.SE])

While large language models (LLMs) are increasingly being used for program
synthesis, they lack the global view needed to develop useful abstractions;
they generally predict programs one at a time, often repeating the same
functionality. Generating redundant code from scratch is both inefficient and
error-prone. To address this, we propose Refactoring for Generalizable
Abstraction Learning (ReGAL), a gradient-free method for learning a library of
reusable functions via code refactorization, i.e. restructuring code without
changing its execution output. ReGAL learns from …

abstractions arxiv code cs.se error global language language models large language large language models llms refactoring synthesis view