mlsanityData to 7 Oct 2026

LionGuard: Building a Contextualized Moderation Classifier to Tackle Localized Unsafe Content

arXiv 2407.10995 ↗First posted 24 Jun 2024cs.CL cs.AIcited by 5

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Abstract (arXiv)

As large language models (LLMs) become increasingly prevalent in a wide variety of applications, concerns about the safety of their outputs have become more significant. Most efforts at safety-tuning or moderation today take on a predominantly Western-centric view of safety, especially for toxic, hateful, or violent speech. In this paper, we describe LionGuard, a Singapore-contextualized moderation classifier that can serve as guardrails against unsafe LLM outputs. When assessed on Singlish data, LionGuard outperforms existing widely-used moderation APIs, which are not finetuned for the Singapore context, by 14% (binary) and up to 51% (multi-label). Our work highlights the benefits of localization for moderation classifiers and presents a practical and scalable approach for low-resource languages.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 22 of its 49 references that are in the map. First posted on arXiv 24 Jun 2024.

Builds on

22 of 49 references.

References · 22

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10 more
  1. 2019SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media (OffensEval)cited by 206
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Built on by

Cited by year

Citing papers per year: 2024: 2; 2025: 3.

5 papers.

  1. 2024WalledEval: A Comprehensive Safety Evaluation Toolkit for Large Language Modelscited by 17
  2. 2025Safe at the Margins: A General Approach to Safety Alignment in Low-Resource English Languages -- A Singlish Case Studycited by 6
  3. 2024A Flexible Large Language Models Guardrail Development Methodology Applied to Off-Topic Prompt Detectioncited by 3
  4. 2025Toxicity-Aware Few-Shot Prompting for Low-Resource Singlish Translationcited by 1
  5. 2025Detecting Offensive Memes with Social Biases in Singapore Context Using Multimodal Large Language Modelscited by 0