Qorgau：评估哈萨克语-俄语双语语境下的LLM安全性
Qorgau: Evaluating LLM Safety in Kazakh-Russian Bilingual Contexts
AI总结：
本文提出专为哈萨克语和俄语双语语境设计的LLM安全性评估数据集Qorgau，实验表明多语言与特定语言模型在安全性上存在显著差异，凸显了开发特定地区数据集的必要性。
摘要：
大型语言模型（LLMs）已知具有生成有害内容的潜力，从而给用户带来风险。尽管在开发LLM风险分类法和安全性评估提示方面取得了重大进展，但大多数研究都集中在以英语为主的单语语境中。然而，双语语境下特定语言和地区的风险往往被忽视，且核心发现可能与单语环境中的结论存在分歧。本文介绍了Qorgau，这是一个专为哈萨克语和俄语安全性评估设计的新型数据集，反映了哈萨克斯坦独特的双语环境，该环境中同时使用哈萨克语（一种低资源语言）和俄语（一种高资源语言）。对多语言和特定语言LLMs进行的实验显示，安全性表现存在显著差异，这强调需要量身定制特定地区的数据集，以确保在哈萨克斯坦等国家负责任且安全地部署LLMs。警告：本文包含可能具有冒犯性、有害或偏见的数据示例。
英文摘要：
Large language models (LLMs) are known to have the potential to generate harmful content, posing risks to users. While significant progress has been made in developing taxonomies for LLM risks and safety evaluation prompts, most studies have focused on monolingual contexts, primarily in English. However, language- and region-specific risks in bilingual contexts are often overlooked, and core findings can diverge from those in monolingual settings. In this paper, we introduce Qorgau, a novel dataset specifically designed for safety evaluation in Kazakh and Russian, reflecting the unique bilingual context in Kazakhstan, where both Kazakh (a low-resource language) and Russian (a high-resource language) are spoken. Experiments with both multilingual and language-specific LLMs reveal notable differences in safety performance, emphasizing the need for tailored, region-specific datasets to ensure the responsible and safe deployment of LLMs in countries like Kazakhstan. Warning: this paper contains example data that may be offensive, harmful, or biased.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
