Summary of Unleashing the Potential Of Large Language Models For Predictive Tabular Tasks in Data Science, by Yazheng Yang et al.
Unleashing the Potential of Large Language Models for Predictive Tabular Tasks in Data Science
by Yazheng Yang, Yuqi Wang, Yaxuan Li, Sankalok Sen, Lei Li, Qi Liu
First submitted to arxiv on: 29 Mar 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 proposed research aims to apply Large Language Models (LLMs) to common predictive tasks in tabular data, such as classification, regression, and imputation of missing values. Despite their natural language comprehension abilities, LLMs struggle with structured tabular data due to a lack of exposure during training. The researchers compile an annotated table corpus and train Llama-2 on this dataset, investigating zero-shot, few-shot, and in-context learning scenarios. Experimental results show significant improvements over existing benchmarks, demonstrating the effectiveness of tailoring LLM training for table-related problems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The research uses special computer models called Large Language Models to solve common problems with data. These models are great at understanding human language, but they struggle when working with tables and structured data. The scientists make a new dataset that includes instructions on how to work with tables and train the model on this data. They then test it in different scenarios and find that it does much better than before. |
Keywords
» Artificial intelligence » Classification » Few shot » Llama » Regression » Zero shot
