ArcheType: A Novel Framework for Open-Source Column Type Annotation using Large Language Models
| Autor: | Feuer, Benjamin, Liu, Yurong, Hegde, Chinmay, Freire, Juliana |
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| Rok vydání: | 2023 |
| Předmět: | |
| Zdroj: | Proceedings of the VLDB Endowment, Volume 17, Issue 9, Pages 2279 - 2292, 2024 |
| Druh dokumentu: | Working Paper |
| DOI: | 10.14778/3665844.3665857 |
| Popis: | 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 CTA 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 a new state-of-the-art performance on zero-shot CTA benchmarks (including three new domain-specific benchmarks which we release along with this paper), and when used in conjunction with classical CTA techniques, it outperforms a SOTA DoDuo model on the fine-tuned SOTAB benchmark. Our code is available at https://github.com/penfever/ArcheType. Comment: VLDB 2024 |
| Databáze: | arXiv |
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