ThaiSafetyBench: Assessing Language Model Safety in Thai Cultural Contexts
The safety evaluation of large language models (LLMs) remains largely centered on English, leaving non-English languages and culturally grounded risks underexplored. In this work, we investigate LLM safety in the context of the Thai language and culture and introduce ThaiSafetyBench, an open-source benchmark comprising 1,954 malicious prompts written in Thai. The dataset covers both general harmful prompts and attacks that are explicitly grounded in Thai cultural, social, and contextual nuances. Using ThaiSafetyBench, we evaluate 24 LLMs, with GPT-4.1 and Gemini-2.5-Pro serving as LLM-as-a-judge evaluators. Our results show that closed-source models generally demonstrate stronger safety p...
| Authors | Trapoom Ukarapol, Nut Chukamphaeng, Kunat Pipatanakul et al. |
|---|---|
| Primary category | cs.CL |
| Published | 5 March 2026 |
| Last updated at source | 6 March 2026 |
Models and datasets referencing this paper
- typhoon-ai/ThaiSafetyBenchDataset
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