Grammar Prompting for Domain-Specific Language Generation with Large
  Language Models
Authors: name: Bailin Wang, name: Zi Wang, name: Xuezhi Wang, name: Yuan Cao, name: Rif A. Saurous, name: Yoon Kim

Large language models (LLMs) can learn to perform a wide range of natural
language tasks from just a handful of in-context examples. However, for
generating strings from highly structured languages (e.g., semantic parsing to
complex domain-specific languages), it is challenging for the LLM to generalize
from just a few exemplars. We explore grammar prompting as a simple
approach for enabling LLMs to use external knowledge and domain-specific
constraints, expressed through a grammar expressed in Backus--Naur Form (BNF),
during in-context learning. Grammar prompting augments each demonstration
example with a specialized grammar that is minimally sufficient for generating
the particular output example, where the specialized grammar is a subset of the
full DSL grammar. For inference, the LLM first predicts a BNF grammar given a
test input, and then generates the output according to the rules of the
grammar. Experiments demonstrate that grammar prompting can enable LLMs to
perform competitively on a diverse set of DSL generation tasks, including
semantic parsing (SMCalFlow, Overnight, GeoQuery), PDDL planning, and even
molecule generation (SMILES).