标题： 有毒性意识的少样本提示用于低资源Singlish翻译 显示英文标题

标题： Toxicity-Aware Few-Shot Prompting for Low-Resource Singlish Translation

摘要： 随着在线交流越来越多地包含代表性不足的语言和口语方言，标准翻译系统往往无法保留本地俚语、混合用语以及有害言论中的文化嵌入标记。 在低资源语言对之间翻译有害内容会带来额外的挑战，这是由于平行数据稀缺以及用于净化攻击性表达的安全过滤器。 在本工作中，我们提出了一种可重复的两阶段毒性保留翻译框架，并在一种混合用语的Singlish安全语料库上进行了演示。 首先，我们进行人工验证的少量提示工程：我们迭代地整理和排序标注者选择的Singlish目标示例，以捕捉细微的俚语、语气和有害性。 其次，我们通过直接翻译和反向翻译的语义相似性对多个大型语言模型进行基准测试，优化模型-提示对。 定量的人工评估证实了我们管道的有效性和效率。 除了提高翻译质量外，我们的框架通过支持文化敏感的监管和在低资源环境中的基准测试，为多文化大型语言模型的安全性做出了贡献。 通过将Singlish作为包容性自然语言处理的试验场，我们强调了在现实世界应用中保留社会语言学细微差别的重要性，例如内容监管和地区平台治理。 显示英文摘要

摘要： As online communication increasingly incorporates under-represented languages and colloquial dialects, standard translation systems often fail to preserve local slang, code-mixing, and culturally embedded markers of harmful speech. Translating toxic content between low-resource language pairs poses additional challenges due to scarce parallel data and safety filters that sanitize offensive expressions. In this work, we propose a reproducible, two-stage framework for toxicity-preserving translation, demonstrated on a code-mixed Singlish safety corpus. First, we perform human-verified few-shot prompt engineering: we iteratively curate and rank annotator-selected Singlish-target examples to capture nuanced slang, tone, and toxicity. Second, we optimize model-prompt pairs by benchmarking several large language models using semantic similarity via direct and back-translation. Quantitative human evaluation confirms the effectiveness and efficiency of our pipeline. Beyond improving translation quality, our framework contributes to the safety of multicultural LLMs by supporting culturally sensitive moderation and benchmarking in low-resource contexts. By positioning Singlish as a testbed for inclusive NLP, we underscore the importance of preserving sociolinguistic nuance in real-world applications such as content moderation and regional platform governance.