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cloud.google.com·7h ago
AI Research
arXiv CS.AI·Aug 6·5 min read
When Large Language Models Know the Table: A Framework for Assessing Data Contamination in Tabular Datasets
TL;DR
A new framework assesses data contamination in tabular datasets used by large language models (LLMs), revealing inflated performance due to prior exposure. Empirical tests on eight datasets showed contamination in four, raising concerns about evaluation reliability.
✦ Why It Matters
Evaluate your tabular datasets for contamination to ensure reliable model performance assessments.
Key Takeaways
- Implement the new framework to assess data contamination in your datasets.
- Use controlled queries to evaluate model performance accurately.
- Compare results against non-neural baselines for better insights.
- Regularly test for contamination to maintain evaluation integrity.
How It Works
The framework generates controlled multiple-choice queries that preserve the task structure while allowing for systematic transformations of the underlying data. These transformations selectively disrupt dataset information, enabling the isolation of performance attributable to contamination.