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arXiv · 2106.11053

Leveraging Language to Learn Program Abstractions and Search Heuristics

Catherine Wong·Kevin Ellis·Joshua B. Tenenbaum·Jacob Andreas
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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.

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Catherine Wong, Kevin Ellis, Joshua B. Tenenbaum, Jacob Andreas. 2022-05-03. Leveraging Language to Learn Program Abstractions and Search Heuristics. https://arxiv.org/abs/2106.11053

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