mlsanityData to 14 Oct 2026

Guardians of Discourse: Evaluating LLMs on Multilingual Offensive Language Detection

arXiv 2410.15623 ↗First posted 21 Oct 2024cs.CLcited by 5

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Abstract (arXiv)

Identifying offensive language is essential for maintaining safety and sustainability in the social media era. Though large language models (LLMs) have demonstrated encouraging potential in social media analytics, they lack thorough evaluation when in offensive language detection, particularly in multilingual environments. We for the first time evaluate multilingual offensive language detection of LLMs in three languages: English, Spanish, and German with three LLMs, GPT-3.5, Flan-T5, and Mistral, in both monolingual and multilingual settings. We further examine the impact of different prompt languages and augmented translation data for the task in non-English contexts. Furthermore, we discuss the impact of the inherent bias in LLMs and the datasets in the mispredictions related to sensitive topics.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 16 of its 33 references that are in the map. First posted on arXiv 21 Oct 2024.

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16 of 33 references.

References · 16

  1. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,380
  2. 2022Scaling Instruction-Finetuned Language Modelscited by 3,024
  3. 2024LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Modelscited by 1,464
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  5. 2023Is ChatGPT better than Human Annotators? Potential and Limitations of ChatGPT in Explaining Implicit Hate Speechcited by 166
  6. 2023Extrapolating Large Language Models to Non-English by Aligning Languagescited by 69
  7. 2020SOLID: A Large-Scale Semi-Supervised Dataset for Offensive Language Identificationcited by 53
  8. 2020Deep Learning Models for Multilingual Hate Speech Detectioncited by 49
  9. 2023When does In-context Learning Fall Short and Why? A Study on Specification-Heavy Taskscited by 37
  10. 2024Large Language Models in Mental Health Care: a Scoping Reviewcited by 34
  11. 2023HARE: Explainable Hate Speech Detection with Step-by-Step Reasoningcited by 31
  12. 2019Evaluating the Cross-Lingual Effectiveness of Massively Multilingual Neural Machine Translationcited by 27
4 more
  1. 2023Generating Valid and Natural Adversarial Examples with Large Language Modelscited by 12
  2. 2023Model-Agnostic Meta-Learning for Multilingual Hate Speech Detectioncited by 11
  3. 2021Cross-lingual Capsule Network for Hate Speech Detection in Social Mediacited by 5
  4. 2024Thinker-DDM: Modeling Deliberation for Machine Translation with a Drift-Diffusion Processcited by 3

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Cited by year

Citing papers per year: 2025: 5.

5 papers.

  1. 2025Lost in Pronunciation: Detecting Chinese Offensive Language Disguised by Phonetic Cloaking Replacementcited by 10
  2. 2025Can Prompting LLMs Unlock Hate Speech Detection across Languages? A Zero-shot and Few-shot Studycited by 7
  3. 2025MedFact: A Large-scale Chinese Dataset for Evidence-based Medical Fact-checking of LLM Responsescited by 6
  4. 2025Artificial Conversations, Real Results: Fostering Language Detection with Synthetic Datacited by 2
  5. 2025Subasa - Adapting Language Models for Low-resourced Offensive Language Detection in Sinhalacited by 0