Matchmaker: Self-Improving Compositional LLM Programs for Table Schema Matching
Keywords: schema matching, tables, data-centric AI, Large Language Models, databases
TL;DR: schema matching across heterogenous tables using self-improving compositional LLM programs
Abstract: Schema matching -- the task of finding matches between attributes across disparate data sources with different tables and hierarchies -- is critical for creating interoperable machine learning (ML)-ready data. Addressing this fundamental data-centric problem has wide implications, especially in domains like healthcare, finance and e-commerce --- but also has the potential to benefit ML models more generally, by increasing the data available for ML model training. However, schema matching is a challenging ML task due to structural/hierarchical and semantic heterogeneity between different table schemas. Previous ML approaches to automate schema matching have either required significant labeled data for model training, which is often unrealistic, or suffer from poor zero-shot performance. To this end, we propose Matchmaker - a compositional language model program for schema matching, comprised of candidate generation, refinement and confidence scoring. Matchmaker is also able to self-improve in a zero-shot manner without the need for labeled demonstrations via a novel optimization approach, which constructs synthetic in-context demonstrations to guide the language model's reasoning process. Empirically, we demonstrate on real-world medical table schema matching benchmarks that Matchmaker outperforms previous ML-based approaches, highlighting its potential to accelerate data integration and interoperability of ML-ready data.
Submission Number: 25
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