Summary of Large Language Models Can Automatically Engineer Features For Few-shot Tabular Learning, by Sungwon Han et al.
Large Language Models Can Automatically Engineer Features for Few-Shot Tabular Learning
by Sungwon Han, Jinsung Yoon, Sercan O Arik, Tomas Pfister
First submitted to arxiv on: 15 Apr 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 This paper presents a novel in-context learning framework, FeatLLM, which leverages Large Language Models (LLMs) to generate optimal input data for tabular predictions. By employing LLMs as feature engineers, FeatLLM produces high-quality features that can be used to infer class likelihood with simple downstream machine learning models like linear regression. The proposed framework eliminates the need for querying LLMs at inference time and only requires API-level access, overcoming prompt size limitations. Compared to existing LLM-based approaches, FeatLLM achieves significant improvements (10% on average) across numerous tabular datasets from various domains. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine if you could use super-smart language models to make predictions about tables of data. That’s what this paper is all about! They created a new way to use these language models, called FeatLLM, which helps generate the best possible data for making predictions. This means that you can use these language models in new and exciting ways, like predicting what will happen next in a table or making smart decisions based on lots of data. The cool thing about this approach is that it’s really fast and efficient, so you can use it to make predictions quickly and accurately. |
Keywords
» Artificial intelligence » Inference » Likelihood » Linear regression » Machine learning » Prompt
