Summary of Matchmaker: Self-improving Large Language Model Programs For Schema Matching, by Nabeel Seedat et al.
Matchmaker: Self-Improving Large Language Model Programs for Schema Matching
by Nabeel Seedat, Mihaela van der Schaar
First submitted to arxiv on: 31 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Schema matching is a crucial task in machine learning (ML) for creating interoperable data. This problem has significant implications in domains like healthcare, finance, and e-commerce, as well as improving ML models’ performance by increasing training data availability. However, schema matching is challenging due to structural/hierarchical and semantic heterogeneity between different schemas. Previous approaches require labeled data or suffer from poor zero-shot performance. To address this, we propose Matchmaker – a compositional language model program for schema matching, consisting of candidate generation, refinement, and confidence scoring. Matchmaker self-improves in a zero-shot manner without labeled demonstrations via a novel optimization approach that constructs synthetic in-context demonstrations to guide the language model’s reasoning process. Our experiments on real-world medical schema matching benchmarks show that Matchmaker outperforms previous ML-based approaches, highlighting its potential to accelerate data integration and interoperability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Schema matching is like trying to find connections between different pieces of information from various sources. This is important because it helps make sure machine learning (ML) models can understand and use the data correctly. Current methods for doing this require a lot of labeled data, which isn’t always available, or they don’t work very well without that data. To solve this problem, we created a new approach called Matchmaker that uses language models to find connections between different pieces of information. This approach can improve itself without needing labeled data and works better than previous methods on real-world medical data. |
Keywords
* Artificial intelligence * Language model * Machine learning * Optimization * Zero shot
