title: Adapting Safe-for-Work Classifier for Malaysian Language Text: Enhancing
    Alignment in LLM-Ops Framework
summary: 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.
paper_authors._id: 66a9c5650ab13bd9ed49c692
paper_authors.name: Aisyah Razak
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paper_authors.name: Ariff Nazhan
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paper_authors.fullname: Ariff Nazhan
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paper_authors.name: Kamarul Adha
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paper_authors.fullname: Kamarul Adha
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paper_authors.name: Wan Adzhar Faiq Adzlan
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paper_authors.fullname: Faiq Adzlan
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paper_authors.user: wanadzhar913
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paper_authors.name: Mas Aisyah Ahmad
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paper_authors._id: 66a9c5650ab13bd9ed49c697
paper_authors.name: Ammar Azman
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paper_authors.fullname: Ammar Azman
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paper_authors.user: ammarxix
paper_title: Adapting Safe-for-Work Classifier for Malaysian Language Text: Enhancing
    Alignment in LLM-Ops Framework
paper_summary: 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.
paper_ai_summary: A novel safe-for-work text classifier for Malaysian language content is developed using a curated dataset of Malaysian text and state-of-the-art natural language processing techniques, enhancing alignment in LLM-Ops.
paper_ai_keywords: natural language processing large language models LLM-Ops safe-for-work classifiers text classification dataset curation text annotation