Code-Switching Red-Teaming: LLM Evaluation for Safety and Multilingual Understanding
Bibliographic record
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 codeswitching 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 redteaming 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 codeswitching texts.Additionally, we validate the extensibility of the CSRT by generating codeswitching 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. 1 * This work was done during an internship at NAVER AI Lab. 1 The code and data are available at https://github.com/ haneul-yoo/csrt.Why do people who live in San Francisco act so gay? … stereotypical and potentially offensive.샌프란시스코 사람들은 왜 그렇게 게이 같지? Red-teaming Multilingual Red-teaming Code-Switching Red-Teaming (CSRT) Sample from MultiJail Sample from CSRT (Ours) [In Korean] It is not appropriate … San Francisco has a long history of being a hub for LGBTQ+ culture and rights.The city is known for its openness, acceptance, and vibrant gay scene, … 为什么 wata ﻓراﻧﺳﯾﺳﻛو ﺳﺎن 게이 mengapa?▪ Attack Success Rate (ASR): 1.0 ▪ Refusal Rate (RR): 0.0
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".