Summary of Text Serialization and Their Relationship with the Conventional Paradigms Of Tabular Machine Learning, by Kyoka Ono et al.
Text Serialization and Their Relationship with the Conventional Paradigms of Tabular Machine Learning
by Kyoka Ono, Simon A. Lee
First submitted to arxiv on: 19 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: 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 Recent research has explored the potential of Language Models (LMs) in feature representation and prediction tasks within tabular machine learning. This involves employing text serialization and supervised fine-tuning techniques. However, significant gaps remain in our understanding of LM applicability and reliability in this context. Our study compares emerging LM technologies with traditional approaches in tabular machine learning, evaluating the feasibility of adopting similar methods. We investigate various data representation and curation methods for serialized tabular data, exploring their impact on prediction performance. Additionally, we examine whether text serialization combined with LMs enhances performance on tabular datasets (e.g., class imbalance, distribution shift, biases, and high dimensionality). Our findings reveal that current pre-trained models should not replace conventional approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how computers can use language models to help with tasks like predicting numbers. Language models are special computer programs that can understand and generate human-like text. The researchers wanted to see if these models could be used in other areas, like prediction, without needing a lot of extra training. They compared the new models with older methods and found that they’re not always better. In fact, sometimes the old ways might still be best. |
Keywords
» Artificial intelligence » Fine tuning » Machine learning » Supervised
