Adapting Safe-for-Work Classifier for Malaysian Language Text: Enhancing
  Alignment in LLM-Ops Framework
Authors: name: Aisyah Razak, name: Ariff Nazhan, name: Kamarul Adha, name: Wan Adzhar Faiq Adzlan, name: Mas Aisyah Ahmad, name: Ammar Azman

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.