Benchmarking LLM Guardrails in Handling Multilingual Toxicity

• URL: http://arxiv.org/abs/2410.22153v1
• Date: Tue, 29 Oct 2024 15:51:24 GMT
• Title: Benchmarking LLM Guardrails in Handling Multilingual Toxicity
• Authors: Yahan Yang, Soham Dan, Dan Roth, Insup Lee,
• Abstract summary: We introduce a comprehensive multilingual test suite, spanning seven datasets and over ten languages, to benchmark the performance of state-of-the-art guardrails. We investigate the resilience of guardrails against recent jailbreaking techniques, and assess the impact of in-context safety policies and language resource availability on guardrails' performance. Our findings show that existing guardrails are still ineffective at handling multilingual toxicity and lack robustness against jailbreaking prompts.
• Score: 57.296161186129545
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: With the ubiquity of Large Language Models (LLMs), guardrails have become crucial to detect and defend against toxic content. However, with the increasing pervasiveness of LLMs in multilingual scenarios, their effectiveness in handling multilingual toxic inputs remains unclear. In this work, we introduce a comprehensive multilingual test suite, spanning seven datasets and over ten languages, to benchmark the performance of state-of-the-art guardrails. We also investigates the resilience of guardrails against recent jailbreaking techniques, and assess the impact of in-context safety policies and language resource availability on guardrails' performance. Our findings show that existing guardrails are still ineffective at handling multilingual toxicity and lack robustness against jailbreaking prompts. This work aims to identify the limitations of guardrails and to build a more reliable and trustworthy LLMs in multilingual scenarios.

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