Summary of Elf-gym: Evaluating Large Language Models Generated Features For Tabular Prediction, by Yanlin Zhang et al.
ELF-Gym: Evaluating Large Language Models Generated Features for Tabular Prediction
by Yanlin Zhang, Ning Li, Quan Gan, Weinan Zhang, David Wipf, Minjie Wang
First submitted to arxiv on: 13 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 proposes a framework called ELF-Gym to evaluate Large Language Model (LLM)-generated features in machine learning pipelines. The authors curated a dataset of 251 “golden” features used by top-performing teams in Kaggle competitions and developed a method to quantify the impact of LLM-generated features on downstream model performance and their alignment with expert-crafted features. This framework provides insights into the disparities between LLMs and human experts, highlighting areas where LLMs can improve. For example, the authors empirically demonstrate that LLMs can semantically capture around 56% of golden features in the best-case scenario, but this drops to 13% at a more demanding implementation level. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how computers and humans are good at making predictions together. It’s like building a team! The researchers created a special tool called ELF-Gym that can compare what computer models do with what experts do when creating features for machine learning. They used old competitions from Kaggle to get 251 good features done by expert teams. Then, they looked at how well the computer models did compared to the experts and found some surprising things! For example, computers are really good at finding simple patterns like colors, but not so good at finding complex patterns that humans can see. |
Keywords
» Artificial intelligence » Alignment » Large language model » Machine learning
