  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 readable programs. In this paper, we introduce LILO,
a neurosymbolic framework that iteratively synthesizes, compresses, and
documents code to build libraries tailored to particular problem domains. LILO
combines LLM-guided program synthesis with recent algorithmic advances in
automated refactoring from Stitch: a symbolic compression system that
efficiently identifies optimal lambda abstractions across large code corpora.
To make these abstractions interpretable, we introduce an auto-documentation
(AutoDoc) procedure that infers natural language names and docstrings based on
contextual examples of usage. In addition to improving human readability, we
find that AutoDoc boosts performance by helping LILO's synthesizer to interpret
and deploy learned abstractions. We evaluate LILO on three inductive program
synthesis benchmarks for string editing, scene reasoning, and graphics
composition. Compared to existing neural and symbolic methods - including the
state-of-the-art library learning algorithm DreamCoder - LILO solves more
complex tasks and learns richer libraries that are grounded in linguistic
knowledge.
