LLMs Show Significant Cross-Lingual Safety Gaps, Especially in Indian Languages.
Key takeaways
- LLM safety alignment is predominantly English-focused, creating vulnerabilities in other languages.
- A new benchmark, INCLUDE, quantifies socio-cultural biases in Indian languages.
- Open-source models showed highest bias in Bengali, while closed-source models showed highest bias in English.
- Cross-lingual safety gaps can lead to stereotype reinforcement and harmful bias propagation.
Who benefits
Summary
Current LLM safety alignments are heavily English-centric, leading to critical failures and biased outputs in non-English languages. A new benchmark, INCLUDE, reveals significant socio-cultural biases in LLMs across various Indian languages.
Why it matters
Professionals deploying LLMs globally must understand that English-centric safety measures are insufficient, risking reputational damage and ethical concerns due to biased outputs in non-English markets.
How to implement this in your domain
- 1Audit existing LLM deployments for non-English language performance and potential bias.
- 2Integrate multilingual and culturally specific safety benchmarks into LLM evaluation pipelines.
- 3Invest in diverse language data collection and annotation for more robust safety training.
- 4Collaborate with linguistic and cultural experts to develop nuanced safety guidelines for global markets.
Original post by Namya Bhatnagar
"arXiv:2608.18131v1 Announce Type: new Abstract: Current safety alignment training for Large Language Models (LLMs) are heavily English-centric. When such safety filters fail for non-English languages, the consequences are immediate and user-facing: voice assistants and spoken dia…"
View on XOriginally posted by Namya Bhatnagar on X · view source
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