Summary of Annotatedtables: a Large Tabular Dataset with Language Model Annotations, by Yaojie Hu et al.
AnnotatedTables: A Large Tabular Dataset with Language Model Annotations
by Yaojie Hu, Ilias Fountalis, Jin Tian, Nikolaos Vasiloglou
First submitted to arxiv on: 24 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a methodology to annotate large amounts of tabular data using Large Language Models (LLMs), overcoming the traditional scalability bottleneck for tabular machine learning. The approach can generate various types of annotations based on specific research objectives, as demonstrated with SQL annotation and input-target column annotation examples. To showcase the value of this methodology and dataset, two follow-up studies are performed: translating SQL programs to Rel programs and evaluating a neural tabular classifier (TabPFN) trained on Bayesian priors. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps computers understand tables by automatically adding annotations. It uses special AI models called Large Language Models (LLMs) to do this job quickly and efficiently. The researchers show that these LLMs can even translate SQL commands into a different programming language, Rel, which is previously unknown to them. Another important finding is that a neural tabular classifier, TabPFN, performs similarly well as another machine learning method, AutoML, when trained on annotated tables. |
Keywords
» Artificial intelligence » Machine learning
