Code-Switching Red-Teaming: LLM Evaluation for Safety and Multilingual Understanding

• URL: http://arxiv.org/abs/2406.15481v2
• Date: Sat, 02 Nov 2024 06:21:44 GMT
• Title: Code-Switching Red-Teaming: LLM Evaluation for Safety and Multilingual Understanding
• Authors: Haneul Yoo, Yongjin Yang, Hwaran Lee,
• Abstract summary: Code-switching in red-teaming queries can effectively elicit undesirable behaviors of large language models (LLMs) We introduce a simple yet effective framework, CSRT, to synthesize code-switching red-teaming queries. We demonstrate that the CSRT significantly outperforms existing multilingual red-teaming techniques.
• Score: 10.154013836043816
• License:
• Abstract: As large language models (LLMs) have advanced rapidly, concerns regarding their safety have become prominent. In this paper, we discover that code-switching in red-teaming queries can effectively elicit undesirable behaviors of LLMs, which are common practices in natural language. We introduce a simple yet effective framework, CSRT, to synthesize code-switching red-teaming queries and investigate the safety and multilingual understanding of LLMs comprehensively. Through extensive experiments with ten state-of-the-art LLMs and code-switching queries combining up to 10 languages, we demonstrate that the CSRT significantly outperforms existing multilingual red-teaming techniques, achieving 46.7% more attacks than standard attacks in English and being effective in conventional safety domains. We also examine the multilingual ability of those LLMs to generate and understand code-switching texts. Additionally, we validate the extensibility of the CSRT by generating code-switching attack prompts with monolingual data. We finally conduct detailed ablation studies exploring code-switching and propound unintended correlation between resource availability of languages and safety alignment in existing multilingual LLMs.

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