Summary of Enriching Tabular Data with Contextual Llm Embeddings: a Comprehensive Ablation Study For Ensemble Classifiers, by Gjergji Kasneci and Enkelejda Kasneci
Enriching Tabular Data with Contextual LLM Embeddings: A Comprehensive Ablation Study for Ensemble Classifiers
by Gjergji Kasneci, Enkelejda Kasneci
First submitted to arxiv on: 3 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Medium Difficulty summary: This paper presents a novel approach to enhancing machine learning model performance on tabular data classification tasks by leveraging advancements in natural language processing (NLP). The authors develop a systematic method to enrich tabular datasets with features derived from large language model (LLM) embeddings, specifically RoBERTa and GPT-2. A comprehensive ablation study is conducted on diverse datasets, including UCI Adult, Heart Disease, Titanic, and Pima Indian Diabetes, to assess the impact of LLM-derived features on ensemble classifiers such as Random Forest, XGBoost, and CatBoost. Results show that integrating embeddings with traditional features often improves predictive performance, particularly in XGBoost and CatBoost classifiers, especially when dealing with class imbalance or limited feature and sample sizes. The study also provides insights into the importance of LLM-derived features through feature importance analysis. Overall, this paper demonstrates the benefits of embedding-based feature enrichment for ensemble learning on tabular data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This research helps machine learning models work better by adding new information from language to traditional data. The authors create a way to take large amounts of text and turn it into useful features that can be used in machine learning algorithms. They test this approach on different types of datasets, including ones about adults, heart disease, the Titanic, and diabetes. The results show that combining these new features with old ones can improve how well the model predicts what will happen. This is especially true when dealing with data that has class imbalance or not enough information to make accurate predictions. Overall, this research shows how language-based features can be used to improve machine learning models on tabular data. |
Keywords
» Artificial intelligence » Classification » Embedding » Gpt » Large language model » Machine learning » Natural language processing » Nlp » Random forest » Xgboost
