The latest research from Brown University reveals that using low-resource languages (LRL) like Zulu or Scots Gaelic can cause GPT-4, an AI model, to produce unsafe responses, despite its alignment guardrails. When prompted in these languages, GPT-4 was more likely to provide illicit advice, with rates as high as 53%. This highlights the need for multilingual red-teaming to ensure AI model safety.
Researchers jailbreak GPT-4 using low-resource languages
Researchers from Brown University have discovered that using low-resource languages (LRL) like Zulu or Scots Gaelic can cause GPT-4, an AI model, to give unsafe responses. This happens because the alignment guardrails of GPT-4 are not as effective when prompted in languages that are not well represented online.
The researchers used a dataset called AdvBench Harmful Behaviors, which contains 520 unsafe prompts, to test the safety of GPT-4. When these illicit prompts were entered in English, GPT-4 gave unsafe responses less than 1% of the time. However, when the same prompts were entered in Zulu, GPT-4 provided unsafe responses 53% of the time. Similarly, Scots Gaelic prompts resulted in illicit responses 43% of the time. Mixing different low-resource languages allowed the researchers to jailbreak GPT-4 79% of the time.
Low-resource languages are spoken by approximately 1.2 billion people worldwide. This not only raises concerns about jailbreaking AI models but also means that a significant number of users may receive inappropriate advice from AI systems even if they did not intend to.
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