Stop Uploading Test Data in Plain Text: Practical Strategies for Mitigating Data Contamination by Evaluation Benchmarks
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Data contamination has become especially prevalent and challenging with the rise of models pretrained on very large, automatically-crawled corpora. For closed models, the training data becomes a trade secret, and even for open models, it is not trivial to ascertain whether a particular test instance has been compromised. Strategies such as live leaderboards with hidden answers, or using test data which is guaranteed to be unseen, are expensive and become fragile with time. Assuming that all relevant actors value clean test data and will cooperate to mitigate data contamination, what can be done? We propose three strategies that can make a difference: (1) Test data made public should be encrypted with a public key and licensed to disallow derivative distribution; (2) demand training exclusion controls from closed API holders, and protect your test data by refusing to evaluate until demands are met; (3) in case of test data based on internet text, avoid data which appears with its solution on the internet, and release the context of internet-derived data along with the data. These strategies are practical and can be effective in preventing data contamination and allowing trustworthy evaluation of models' capabilities.
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