Summary of Elephants Never Forget: Testing Language Models For Memorization Of Tabular Data, by Sebastian Bordt et al.
Elephants Never Forget: Testing Language Models for Memorization of Tabular Data
by Sebastian Bordt, Harsha Nori, Rich Caruana
First submitted to arxiv on: 11 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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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 investigates the application of Large Language Models (LLMs) to tabular data, highlighting concerns about data contamination and memorization. The authors introduce various techniques to assess contamination and memorization, revealing that LLMs are often pre-trained on popular tabular datasets. This exposure can lead to invalid performance evaluation on downstream tasks due to overfitting. Interestingly, the study identifies a regime where the language model reproduces important statistics but fails to reproduce the dataset verbatim. The findings emphasize the need for ensuring data integrity in machine learning tasks with LLMs. To facilitate future research, an open-source tool is released to perform various tests for memorization. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models (LLMs) are powerful tools that can be applied to many different tasks. However, they have some serious flaws. For example, they can learn the names and values of features in tabular data, but this learning is not always accurate. In fact, LLMs can even memorize entire datasets! This paper looks at these issues and shows how they can affect the performance of downstream tasks. The authors also release a tool to help researchers identify when an LLM has memorized a dataset. |
Keywords
* Artificial intelligence * Language model * Machine learning * Overfitting
