FanarGuard: 一种文化感知的阿拉伯语言模型审查过滤器
FanarGuard: A Culturally-Aware Moderation Filter for Arabic Language Models
AI总结：
FanarGuard是一种双语审查过滤器，通过评估阿拉伯语和英语中的安全性和文化对齐性，提升内容审查的准确性与文化敏感性。
摘要：
内容审查过滤器是防止语言模型对齐失败的关键保障。然而，大多数现有过滤器仅关注通用安全，忽视了文化背景。在本工作中，我们介绍了FanarGuard，一种双语审查过滤器，能够评估阿拉伯语和英语中的安全性和文化对齐性。我们构建了一个包含超过468,000个提示和响应对的数据集，这些数据来自合成和公开数据集，由一组LLM法官根据无害性和文化意识进行评分，并利用该数据集训练了两种过滤器变体。为了严格评估文化对齐性，我们进一步开发了首个针对阿拉伯文化背景的基准，包含超过1000个规范敏感的提示，其LLM生成的响应由人类评估者注释。结果表明，FanarGuard在与人类注释的一致性方面优于跨注释可靠性，同时在安全基准测试中与最先进的过滤器性能相当。这些发现突显了将文化意识整合到审查中的重要性，并确立了FanarGuard作为更上下文敏感保障的实用步骤。
英文摘要：
Content moderation filters are a critical safeguard against alignment failures in language models. Yet most existing filters focus narrowly on general safety and overlook cultural context. In this work, we introduce FanarGuard, a bilingual moderation filter that evaluates both safety and cultural alignment in Arabic and English. We construct a dataset of over 468K prompt and response pairs, drawn from synthetic and public datasets, scored by a panel of LLM judges on harmlessness and cultural awareness, and use it to train two filter variants. To rigorously evaluate cultural alignment, we further develop the first benchmark targeting Arabic cultural contexts, comprising over 1k norm-sensitive prompts with LLM-generated responses annotated by human raters. Results show that FanarGuard achieves stronger agreement with human annotations than inter-annotator reliability, while matching the performance of state-of-the-art filters on safety benchmarks. These findings highlight the importance of integrating cultural awareness into moderation and establish FanarGuard as a practical step toward more context-sensitive safeguards.
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。
