Elephants Never Forget: Memorization and Learning of Tabular Data in Large Language Models
(2404.06209)Abstract
While many have shown how LLMs can be applied to a diverse set of tasks, the critical issues of data contamination and memorization are often glossed over. In this work, we address this concern for tabular data. Specifically, we introduce a variety of different techniques to assess whether a language model has seen a tabular dataset during training. This investigation reveals that LLMs have memorized many popular tabular datasets verbatim. We then compare the few-shot learning performance of LLMs on datasets that were seen during training to the performance on datasets released after training. We find that LLMs perform better on datasets seen during training, indicating that memorization leads to overfitting. At the same time, LLMs show non-trivial performance on novel datasets and are surprisingly robust to data transformations. We then investigate the in-context statistical learning abilities of LLMs. Without fine-tuning, we find them to be limited. This suggests that much of the few-shot performance on novel datasets is due to the LLM's world knowledge. Overall, our results highlight the importance of testing whether an LLM has seen an evaluation dataset during pre-training. We make the exposure tests we developed available as the tabmemcheck Python package at https://github.com/interpretml/LLM-Tabular-Memorization-Checker
Overview
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This study investigates the memorization of tabular data in LLMs, specifically GPT-3.5 and GPT-4, and its implications on their learning capabilities.
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Four unique memorization tests are used to assess if LLMs have memorized datasets during training, revealing robust memorization of popular datasets.
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The research examines the impact of memorization on few-shot learning performances, showing better performance on memorized datasets and reasonable capabilities on novel datasets.
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It highlights the potential of LLMs in generating novel samples from memorized datasets for synthetic data generation, despite limitations in statistical prediction abilities.
Memorization and Learning of Tabular Data in LLMs: An Investigation
Introduction
Recent advancements in LLMs have extended their utility beyond traditional natural language processing tasks, encompassing structured learning, time-series forecasting, and particularly the handling of tabular data. Despite their remarkable versatility, concerns about data contamination and memorization in LLMs, especially regarding tabular datasets, have not been thoroughly addressed. This study systematically examines the extent to which LLMs, specifically GPT-3.5 and GPT-4, memorize tabular data and the implications of such memorization on their few-shot learning capabilities.
Memorization in LLMs
The study introduces and employs four distinct memorization tests to detect whether LLMs have seen and memorized tabular datasets during training. These tests range from the Header Test, which evaluates the model's ability to complete dataset rows verbatim, to the Feature Completion Test, assessing LLMs' capability to predict highly unique feature values. Results indicate a robust memorization of popular tabular datasets, including the complete generation of datasets like Iris and Wine from the UCI machine learning repository by GPT-4.
Impact on Few-Shot Learning
The investigation extends to comparing the few-shot learning performance of LLMs on both memorized and novel datasets. It was found that LLMs exhibit superior performance on datasets encountered during training, attributing this to memorization leading to overfitting. Conversely, on novel datasets, LLMs demonstrate reasonable performance, retaining robustness against data transformations such as minor perturbations in numerical values. This distinction emphasizes the necessity of discerning between datasets seen during training and truly new datasets to evaluate LLMs accurately.
LLMs as In-Context Statistical Predictors
The study explore LLMs' in-context statistical learning abilities, revealing limitations in their capacity to perform statistical prediction without specific fine-tuning. Through structured experimentation, it was found that while GPT-4 shows an incremental improvement in performance with an increased number of few-shot examples, both GPT-3.5 and GPT-4 struggle as the dimensionality of the problem increases. This suggests that LLMs' few-shot learning performance largely hinges on their pre-existing world knowledge.
Generating Samples from Memorized Datasets
An interesting facet of this research is the demonstration that GPT-3.5 and GPT-4 can generate novel samples from datasets they have memorized during training. This ability showcases a novel application of LLMs in generating data points that adhere to the statistical properties of the original dataset, offering potential utility in synthetic data generation fields.
Conclusion and Future Directions
This comprehensive study highlights the critical issue of memorization in LLMs, particularly concerning tabular data, and its ramifications on few-shot learning performance and generalization capabilities. By rigorously examining the extents of memorization and its impacts, the research presents a call for more nuanced evaluation methodologies that account for potential data contamination. As LLMs continue to evolve and find applications across diverse data modalities, understanding and mitigating the effects of memorization will be crucial in ensuring their accurate and ethical use.
Future research should aim to refine the detection methods of memorization and explore mechanisms to reduce or control the overfitting induced by data contamination. The development of LLMs that can leverage their world knowledge without falling prey to memorization artifacts remains a pivotal challenge for the AI research community.
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