Benjamin Feuer, Yurong Liu, Chinmay Hegde, Juliana Freire

Existing deep-learning approaches to semantic column type annotation (CTA) have important shortcomings: they rely on semantic types which are fixed at training time; require a large number of training samples per type and incur large run-time inference costs; and their performance can degrade when evaluated on novel datasets, even when types remain constant. Large language models have exhibited strong zero-shot classification performance on a wide range of tasks and in this paper we explore their use for CTA. We introduce ArcheType, a simple, practical method for context sampling, prompt serialization, model querying, and label remapping, which enables large language models to solve column type annotation problems in a fully zero-shot manner. We ablate each component of our method separately, and establish that improvements to context sampling and label remapping provide the most consistent gains. ArcheType establishes new state-of-the-art performance on both zero-shot and fine-tuned CTA, including three new domain-specific benchmarks, which we release, along with the code to reproduce our results at https://github.com/penfever/ArcheType.