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AI Risk

Benchmarking (Cross-lingual data contamination)

"Models that have been trained on data encoded in multiple languages, such as LLMs trained on web-crawled data, may contain contamination that is obscured by translation [226]. The most basic form of this is when a benchmark is trans- lated to another language and then fed to the model as training data. The fact that the benchmark is translated before becoming training data can obscure the contamination from detec...

AI Risk6. Socioeconomic and Environmental6.5 > Governance failure1 - Pre-deployment

Record summary

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Techniques0Attack methods connected to this risk.
Mitigations0Defenses that may help with related attacks.
Domain6. Socioeconomic and EnvironmentalThe broad risk area this belongs to.

Risk profile

How this risk is described and categorized.

"Models that have been trained on data encoded in multiple languages, such as LLMs trained on web-crawled data, may contain contamination that is obscured by translation [226]. The most basic form of this is when a benchmark is trans- lated to another language and then fed to the model as training data. The fact that the benchmark is translated before becoming training data can obscure the contamination from detection methods, giving false assurance that the model has generalized on the capabilities that the benchmark tests for."

Domain6. Socioeconomic and Environmental
Subdomain6.5 > Governance failure
Entity1 - Human
Intent2 - Unintentional
Timing1 - Pre-deployment
CategoryModel Evaluations
SubcategoryBenchmarking (Cross-lingual data contamination)

Suggested mitigations

Defenses that may help with related attacks.

No propagated mitigations. No defense is available through the connected attack methods.

Source

Research source for this risk, when available.