Leveraging Pre-existing Coding Abilities of Large Language Models to Improve In-context Learning for Semantic Parsing

Using general-purpose programming languages like Python instead of domain-specific languages, and augmenting prompts with structured domain descriptions, can dramatically improve the accuracy of in-context learning for semantic parsing, especially on compositional generalization tasks.