Summary of Dynamic and Adaptive Feature Generation with Llm, by Xinhao Zhang et al.
Dynamic and Adaptive Feature Generation with LLM
by Xinhao Zhang, Jinghan Zhang, Banafsheh Rekabdar, Yuanchun Zhou, Pengfei Wang, Kunpeng Liu
First submitted to arxiv on: 4 Jun 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 Machine learning algorithms rely heavily on the quality of feature engineering, which transforms raw data into an optimized feature space conducive to model training. However, current methodologies often suffer from a lack of explainability, limited applicability, and inflexible strategy, hindering their deployment across varied scenarios. To address these challenges, our research introduces a novel approach that adopts large language models (LLMs) and feature-generating prompts to enhance the interpretability of the feature generation process. Our dynamic and adaptive method broadens the applicability across various data types and tasks, offering strategic flexibility. A range of experiments demonstrates that our approach significantly outperforms existing methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning is a powerful tool for computers to learn from data. But it needs high-quality data to work well. This paper talks about how we can improve this process by using special language models and prompts. These tools help us understand why the computer is choosing certain features, making the process more transparent. Our method is flexible and works with different types of data and tasks, which makes it useful for many situations. |
Keywords
» Artificial intelligence » Feature engineering » Machine learning
