Summary of Enhancing Table Representations with Llm-powered Synthetic Data Generation, by Dayu Yang et al.
Enhancing Table Representations with LLM-powered Synthetic Data Generation
by Dayu Yang, Natawut Monaikul, Amanda Ding, Bozhao Tan, Kishore Mosaliganti, Giri Iyengar
First submitted to arxiv on: 4 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 novel approach to generating synthetic tabular data for improving table management, discovery, and analysis. It defines a clear concept of table similarity in the context of data transformation activities and uses Large Language Models (LLMs) to create a large-scale synthetic dataset tailored for table-level representation learning. The generated synthetic data aligns with the proposed definition of table similarity and enhances table representations, leading to improved recommendation performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates fake data that looks like real tables to help computers understand tables better. This is important because we make decisions based on data, and having good table understanding can improve decision-making. The researchers use special computer models called Large Language Models (LLMs) to create this fake data. They then tested the fake data and found that it helped improve how well computers could recommend what tables to use. |
Keywords
» Artificial intelligence » Representation learning » Synthetic data
