Leveraging Language to Learn Program Abstractions and Search Heuristics
Catherine Wong, Kevin M Ellis, Joshua Tenenbaum, Jacob Andreas
Abstract
Inductive program synthesis, or inferring programs from examples of desired behavior, offers a general paradigm for building interpretable, robust, andgeneralizable machine learning systems. Effective program synthesis depends on two key ingredients: a strong library of functions from which to build programs, and an efficient search strategy for finding programs that solve a given task. We introduce LAPS (Language for Abstraction and Program Search), a technique for using natural language annotations to guide joint learning of libraries and neurally-guided search models for synthesis. When integrated into a state-of-the-art library learning system (DreamCoder), LAPS produces higher-quality libraries and improves search efficiency and generalization on three domains {–} string editing, image composition, and abstract reasoning about scenes {–} even when no natural language hints are available at test time.
BibTeX
@InProceedings{pmlr-v139-wong21a,
title = {Leveraging Language to Learn Program Abstractions and Search Heuristics},
author = {Wong, Catherine and Ellis, Kevin M and Tenenbaum, Joshua and Andreas, Jacob},
booktitle = {Proceedings of the 38th International Conference on Machine Learning},
pages = {11193--11204},
year = {2021},
editor = {Meila, Marina and Zhang, Tong},
volume = {139},
series = {Proceedings of Machine Learning Research},
month = {18--24 Jul},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v139/wong21a/wong21a.pdf},
url = {https://proceedings.mlr.press/v139/wong21a.html},
abstract = {Inductive program synthesis, or inferring programs from examples of desired behavior, offers a general paradigm for building interpretable, robust, andgeneralizable machine learning systems. Effective program synthesis depends on two key ingredients: a strong library of functions from which to build programs, and an efficient search strategy for finding programs that solve a given task. We introduce LAPS (Language for Abstraction and Program Search), a technique for using natural language annotations to guide joint learning of libraries and neurally-guided search models for synthesis. When integrated into a state-of-the-art library learning system (DreamCoder), LAPS produces higher-quality libraries and improves search efficiency and generalization on three domains {–} string editing, image composition, and abstract reasoning about scenes {–} even when no natural language hints are available at test time.}
}