OpenTab: Advancing Large Language Models as Open-domain Table Reasoners

1 code implementation • 22 Feb 2024 • Kezhi Kong, Jiani Zhang, Zhengyuan Shen, Balasubramaniam Srinivasan, Chuan Lei, Christos Faloutsos, Huzefa Rangwala, George Karypis

Large Language Models (LLMs) trained on large volumes of data excel at various natural language tasks, but they cannot handle tasks requiring knowledge that has not been trained on previously.

Retrieval

Which Examples to Annotate for In-Context Learning? Towards Effective and Efficient Selection

1 code implementation • 30 Oct 2023 • Costas Mavromatis, Balasubramaniam Srinivasan, Zhengyuan Shen, Jiani Zhang, Huzefa Rangwala, Christos Faloutsos, George Karypis

Large Language Models (LLMs) can adapt to new tasks via in-context learning (ICL).

Active Learning In-Context Learning

NameGuess: Column Name Expansion for Tabular Data

1 code implementation • 19 Oct 2023 • Jiani Zhang, Zhengyuan Shen, Balasubramaniam Srinivasan, Shen Wang, Huzefa Rangwala, George Karypis

Recent advances in large language models have revolutionized many sectors, including the database industry.

Text Generation

Mixed-Type Tabular Data Synthesis with Score-based Diffusion in Latent Space

1 code implementation • 14 Oct 2023 • Hengrui Zhang, Jiani Zhang, Balasubramaniam Srinivasan, Zhengyuan Shen, Xiao Qin, Christos Faloutsos, Huzefa Rangwala, George Karypis

Recent advances in tabular data generation have greatly enhanced synthetic data quality.