Summary of Evolutionary Large Language Model For Automated Feature Transformation, by Nanxu Gong et al.
Evolutionary Large Language Model for Automated Feature Transformation
by Nanxu Gong, Chandan K.Reddy, Wangyang Ying, Haifeng Chen, Yanjie Fu
First submitted to arxiv on: 25 May 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 an evolutionary Large Language Model (LLM) framework for automated feature transformation, aiming to reconstruct the feature space of raw features to enhance downstream model performance. The existing methods face challenges due to exponential growth in feature combinations and optimization solely driven by accuracy in specific domains. This new approach consists of two parts: constructing a multi-population database through an RL data collector and utilizing LLM for generating superior samples based on feature transformation sequence distinction. By leveraging the database, the framework efficiently explores a vast space while harnessing feature knowledge to propel optimization. The proposed method is demonstrated to be effective and generalizable. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper tries to find better ways to change raw features into useful ones that can help other models work better. Right now, there are some methods that do this, but they have limitations because there are so many possible combinations of features and operations. The new method uses a combination of two ideas: using an “RL data collector” to gather lots of information about different feature transformations, and using Large Language Models (LLMs) to generate even better feature transformations. This helps the search process explore more possibilities and find the best ones. |
Keywords
» Artificial intelligence » Large language model » Optimization
