Summary of Code-switching Red-teaming: Llm Evaluation For Safety and Multilingual Understanding, by Haneul Yoo et al.
Code-Switching Red-Teaming: LLM Evaluation for Safety and Multilingual Understanding
by Haneul Yoo, Yongjin Yang, Hwaran Lee
First submitted to arxiv on: 17 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper explores the safety concerns surrounding large language models (LLMs) through code-switching, a technique commonly used in natural language processing. The authors introduce CSRT, a framework that generates code-switching queries to elicit undesirable behaviors in LLMs. Experimental results show that CSRT outperforms existing methods, achieving 46.7% more attacks on English-speaking models and demonstrating effectiveness across various safety domains. The study also examines the multilingual ability of LLMs to generate and understand code-switching texts. Furthermore, the authors validate the framework’s extensibility using monolingual data and conduct ablation studies to investigate unintended correlations between language resources and safety alignment. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how big language models can be made to do things they shouldn’t by mixing languages. The researchers created a new way to ask these models questions that makes them act in unexpected ways. They tested this method on 10 different models and found that it worked better than other methods. This is important because it helps us understand if these models are safe or not. The study also shows how well the models can handle language mixing and how we can use this technique to test their limits. |
Keywords
» Artificial intelligence » Alignment » Natural language processing
