Leveraging Language to Learn Program Abstractions and Search Heuristics
Abstract
Inductive program synthesis, or inferring programs from examples of desired behavior, offers a general paradigm for building interpretable, robust, and generalizable 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.
Cite
@article{arxiv.2106.11053,
title = {Leveraging Language to Learn Program Abstractions and Search Heuristics},
author = {Catherine Wong and Kevin Ellis and Joshua B. Tenenbaum and Jacob Andreas},
journal= {arXiv preprint arXiv:2106.11053},
year = {2022}
}
Comments
appeared in Thirty-eighth International Conference on Machine Learning (ICML 2021)