33 citations · 55 across the 4 of their papers we have counts for
4 papers
LILO: Learning Interpretable Libraries by Compressing and Documenting Code
Gabriel Grand, Lionel Wong, Maddy Bowers +4
While large language models (LLMs) now excel at code generation, a key aspect of software development is the art of refactoring: consolidating code into libraries of reusable and r…
Top-Down Synthesis for Library Learning
Matthew Bowers, Theo X. Olausson, Lionel Wong +4
This paper introduces corpus-guided top-down synthesis as a mechanism for synthesizing library functions that capture common functionality from a corpus of programs in a domain spe…
Language Models Can Teach Themselves to Program Better
Patrick Haluptzok, Matthew Bowers, Adam Tauman Kalai
Recent Language Models (LMs) achieve breakthrough performance in code generation when trained on human-authored problems, even solving some competitive-programming problems. Self-p…
Representing Partial Programs with Blended Abstract Semantics
Maxwell Nye, Yewen Pu, Matthew Bowers +3
Synthesizing programs from examples requires searching over a vast, combinatorial space of possible programs. In this search process, a key challenge is representing the behavior o…