As large language models (LLMs) become increasingly integrated into operational workflows (LLM-Ops), there is a pressing need for effective guardrails to ensure safe and aligned interactions, including the ability to detect potentially unsafe or inappropriate content across languages. However, existing safe-for-work classifiers are primarily focused on English text. To address this gap for the Malaysian language, we present a novel safe-for-work text classifier tailored specifically for Malaysian language content. By curating and annotating a first-of-its-kind dataset of Malaysian text spanning multiple content categories, we trained a classification model capable of identifying potentially unsafe material using state-of-the-art natural language processing techniques. This work represents an important step in enabling safer interactions and content filtering to mitigate potential risks and ensure responsible deployment of LLMs. To maximize accessibility and promote further research towards enhancing alignment in LLM-Ops for the Malaysian context, the model is publicly released at https://huggingface.co/malaysia-ai/malaysian-sfw-classifier.

翻译：随着大语言模型（LLMs）日益融入运营工作流（LLM-Ops），迫切需要有效的防护机制来确保安全且对齐的交互，包括具备跨语言检测潜在不安全或不适当内容的能力。然而，现有的安全工作分类器主要针对英语文本。为填补马来西亚语言在此领域的空白，我们提出了一种专门针对马来西亚语言内容的新型安全工作文本分类器。通过整理并标注首个涵盖多内容类别的马来西亚文本数据集，我们利用先进的自然语言处理技术训练了一个能够识别潜在不安全材料的分类模型。此项工作代表了在实现更安全的交互和内容过滤以降低潜在风险、确保LLMs负责任部署方面迈出的重要一步。为最大限度地提高可访问性并促进进一步研究以增强马来西亚语境下LLM-Ops的对齐性，该模型已在 https://huggingface.co/malaysia-ai/malaysian-sfw-classifier 公开发布。