Summary of Gradient Boosting Trees and Large Language Models For Tabular Data Few-shot Learning, by Carlos Huertas
Gradient Boosting Trees and Large Language Models for Tabular Data Few-Shot Learning
by Carlos Huertas
First submitted to arxiv on: 6 Nov 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 The paper investigates the application of Large Language Models (LLMs) in few-shot-learning (FSL) tasks on tabular data (TD). Recent studies have shown that TabLLM is a powerful mechanism for FSL, even surpassing traditional gradient boosting decision trees (GBDT) methods. The authors demonstrate that while LLMs are a viable alternative, the baselines used to gauge performance can be improved upon. By replicating public benchmarks and using their proposed methodology, they improve LightGBM by 290%, primarily driven by forcing node splitting with few samples, a critical step in FSL with GBDT. The results show that TabLLM has an advantage for 8 or fewer shots, but as the number of samples increases, GBDT provides competitive performance at a fraction of runtime. Furthermore, the authors find that FSL is still useful to improve model diversity and, when combined with ExtraTrees, provides strong resilience to overfitting, validated in a machine learning competition setting where they ranked first place. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores how large language models can be used for few-shot-learning on tables of data. It shows that these models are really good at doing this, even better than other methods like gradient boosting decision trees. The authors did some experiments and found that they could make the LightGBM model do much better by forcing it to split into smaller groups with just a few samples. This is important because it helps the model learn faster and be more accurate. They also found that using these language models can help improve how well other models work together, which is useful for avoiding overfitting. |
Keywords
» Artificial intelligence » Boosting » Few shot » Machine learning » Overfitting
