mlsanityData to 7 Oct 2026

Taiwan Safety Benchmark and Breeze Guard: Toward Trustworthy AI for Taiwanese Mandarin

arXiv 2603.07286 ↗First posted 7 Mar 2026cs.CLcited by 0

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Abstract (arXiv)

Global safety models exhibit strong performance across widely used benchmarks, yet their training data rarely captures the cultural and linguistic nuances of Taiwanese Mandarin. This limitation results in systematic blind spots when interpreting region-specific risks such as localized financial scams, culturally embedded hate speech, and misinformation patterns. To address these gaps, we introduce TS-Bench (Taiwan Safety Benchmark), a standardized evaluation suite for assessing safety performance in Taiwanese Mandarin. TS-Bench contains 400 human-curated prompts spanning critical domains including financial fraud, medical misinformation, social discrimination, and political manipulation. In parallel, we present Breeze Guard, an 8B safety model derived from Breeze 2, our previously released general-purpose Taiwanese Mandarin LLM with strong cultural grounding from its original pre-training corpus. Breeze Guard is obtained through supervised fine-tuning on a large-scale, human-verified synthesized dataset targeting Taiwan-specific harms. Our central hypothesis is that effective safety detection requires the cultural grounding already present in the base model; safety fine-tuning alone is insufficient to introduce new socio linguistic knowledge from scratch. Empirically, Breeze Guard significantly outperforms the leading 8B general-purpose safety model, Granite Guardian 3.3, on TS-Bench (+0.17 overall F1), with particularly large gains in high-context categories such as scam (+0.66 F1) and financial malpractice (+0.43 F1). While the model shows slightly lower performance on English-centric benchmarks (ToxicChat, AegisSafetyTest), this tradeoff is expected for a regionally specialized safety model optimized for Taiwanese Mandarin. Together, Breeze Guard and TS-Bench establish a new foundation for trustworthy AI deployment in Taiwan.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 24 of its 31 references that are in the map. First posted on arXiv 7 Mar 2026.

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References · 24

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
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  3. 2024HarmBench: A Standardized Evaluation Framework for Automated Red Teaming and Robust Refusalcited by 1,330
  4. 2023BeaverTails: Towards Improved Safety Alignment of LLM via a Human-Preference Datasetcited by 828
  5. 2022Fine-Tuning can Distort Pretrained Features and Underperform Out-of-Distributioncited by 722
  6. 2021Deduplicating Training Data Makes Language Models Bettercited by 705
  7. 2023Tree of Attacks: Jailbreaking Black-Box LLMs Automaticallycited by 619
  8. 2023XSTest: A Test Suite for Identifying Exaggerated Safety Behaviours in Large Language Modelscited by 477
  9. 2022A Holistic Approach to Undesired Content Detection in the Real Worldcited by 337
  10. 2023ToxicChat: Unveiling Hidden Challenges of Toxicity Detection in Real-World User-AI Conversationcited by 224
  11. 2024ShieldGemma: Generative AI Content Moderation Based on Gemmacited by 224
  12. 2024WildTeaming at Scale: From In-the-Wild Jailbreaks to (Adversarially) Safer Language Modelscited by 206
12 more
  1. 2024AEGIS: Online Adaptive AI Content Safety Moderation with Ensemble of LLM Expertscited by 126
  2. 2023SimpleSafetyTests: a Test Suite for Identifying Critical Safety Risks in Large Language Modelscited by 72
  3. 2023Ignore This Title and HackAPrompt: Exposing Systemic Vulnerabilities of LLMs through a Global Scale Prompt Hacking Competitioncited by 65
  4. 2025The State of Multilingual LLM Safety Research: From Measuring the Language Gap to Mitigating Itcited by 42
  5. 2023Taiwan LLM: Bridging the Linguistic Divide with a Culturally Aligned Language Modelcited by 29
  6. 2025The Scales of Justitia: A Comprehensive Survey on Safety Evaluation of LLMscited by 24
  7. 2024A Survey of Multimodal Sarcasm Detectioncited by 19
  8. 2024Attack Atlas: A Practitioner's Perspective on Challenges and Pitfalls in Red Teaming GenAIcited by 14
  9. 2022Predicting Fine-Tuning Performance with Probingcited by 13
  10. 2023Extending the Pre-Training of BLOOM for Improved Support of Traditional Chinese: Models, Methods and Resultscited by 10
  11. 2024Cross-Lingual Transfer of Debiasing and Detoxification in Multilingual LLMs: An Extensive Investigationcited by 5
  12. 2025The Breeze 2 Herd of Models: Traditional Chinese LLMs Based on Llama with Vision-Aware and Function-Calling Capabilitiescited by 4

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