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cs.CL2025
LLMs Lost in Translation: M-ALERT uncovers Cross-Linguistic Safety Inconsistencies
Felix Friedrich, Simone Tedeschi, Patrick Schramowski +5
Building safe Large Language Models (LLMs) across multiple languages is essential in ensuring both safe access and linguistic diversity. To this end, we conduct a large-scale, comp…
cs.CL2024
ALERT: A Comprehensive Benchmark for Assessing Large Language Models' Safety through Red Teaming
Simone Tedeschi, Felix Friedrich, Patrick Schramowski +4
When building Large Language Models (LLMs), it is paramount to bear safety in mind and protect them with guardrails. Indeed, LLMs should never generate content promoting or normali…
cs.CL2024
Introducing v0.5 of the AI Safety Benchmark from MLCommons
Bertie Vidgen, Adarsh Agrawal, Ahmed M. Ahmed +97
This paper introduces v0.5 of the AI Safety Benchmark, which has been created by the MLCommons AI Safety Working Group. The AI Safety Benchmark has been designed to assess the safe…