Summary of “oh Llm, I’m Asking Thee, Please Give Me a Decision Tree”: Zero-shot Decision Tree Induction and Embedding with Large Language Models, by Ricardo Knauer et al.
“Oh LLM, I’m Asking Thee, Please Give Me a Decision Tree”: Zero-Shot Decision Tree Induction and Embedding with Large Language Models
by Ricardo Knauer, Mario Koddenbrock, Raphael Wallsberger, Nicholas M. Brisson, Georg N. Duda, Deborah Falla, David W. Evans, Erik Rodner
First submitted to arxiv on: 27 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); 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 A novel approach to generating interpretable machine learning models is presented, leveraging large language models’ (LLMs) prior knowledge to induce decision trees without any training data. This zero-shot technique can surpass data-driven tree performance on small-sized tabular datasets and yields comparable embeddings for average performance. The proposed method serves as a strong baseline for low-data regime machine learning methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are powerful tools that can be used to predict results when there is limited data available. A new way of using these models is shown, which allows them to create decision trees without any training data. These zero-shot decision trees can perform as well or even better than ones created with training data on small datasets. This new approach provides a strong starting point for machine learning methods that work well in situations where there isn’t much data. |
Keywords
» Artificial intelligence » Machine learning » Zero shot
