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Adapting Safe-for-Work Classifier for Malaysian Language Text: Enhancing Alignment in LLM-Ops Framework

Aisyah Razak, Ariff Nazhan, Kamarul Adha, Wan Adzhar Faiq Adzlan, Mas Aisyah Ahmad, Ammar Azman

2024-07-31

Adapting Safe-for-Work Classifier for Malaysian Language Text: Enhancing Alignment in LLM-Ops Framework

Summary

This paper discusses the creation of a safe-for-work text classifier specifically designed for the Malaysian language. The classifier helps identify potentially inappropriate content to ensure safer interactions with large language models (LLMs).

What's the problem?

As large language models are used more widely, there's a need for effective systems to filter out unsafe or inappropriate content. Most existing classifiers are focused on English, leaving a gap for languages like Malay. This means that users of Malaysian language content might not have adequate protection against harmful material.

What's the solution?

To fill this gap, the authors developed a new safe-for-work classifier tailored for Malaysian text. They created a unique dataset of Malaysian text that covers various content categories and trained the classifier using advanced natural language processing techniques. This allows the model to effectively detect and filter out unsafe content in the Malaysian language, ensuring that users can interact safely with LLMs.

Why it matters?

This research is important because it enhances the safety of online interactions for Malaysian speakers. By providing a tool that can accurately identify inappropriate content in their language, it promotes responsible use of AI technologies and helps create a safer online environment for everyone.

Abstract

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.