# Harnessing Artificial Intelligence to Combat Online Hate: Exploring the Challenges and Opportunities of Large Language Models in Hate Speech Detection

arXiv: https://arxiv.org/abs/2403.08035 · HTML: https://mlsanity.com/paper/2403.08035/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 46 references that are in the map.
- First posted on arXiv 12 Mar 2024.

## Builds on (most cited first, 28 of 28)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Lost in the Middle: How Language Models Use Long Contexts](https://mlsanity.com/paper/2307.03172/) (2023)
- [Scaling Instruction-Finetuned Language Models](https://mlsanity.com/paper/2210.11416/) (2022)
- [Cross-lingual Language Model Pretraining](https://mlsanity.com/paper/1901.07291/) (2019)
- [ChatGPT Outperforms Crowd-Workers for Text-Annotation Tasks](https://mlsanity.com/paper/2303.15056/) (2023)
- [How to Fine-Tune BERT for Text Classification?](https://mlsanity.com/paper/1905.05583/) (2019)
- [HateCheck: Functional Tests for Hate Speech Detection Models](https://mlsanity.com/paper/2012.15606/) (2020)
- [Is ChatGPT better than Human Annotators? Potential and Limitations of ChatGPT in Explaining Implicit Hate Speech](https://mlsanity.com/paper/2302.07736/) (2023)
- [PhoBERT: Pre-trained language models for Vietnamese](https://mlsanity.com/paper/2003.00744/) (2020)
- [Can ChatGPT Reproduce Human-Generated Labels? A Study of Social Computing Tasks](https://mlsanity.com/paper/2304.10145/) (2023)
- [Detecting Hate Speech with GPT-3](https://mlsanity.com/paper/2103.12407/) (2021)
- [Testing the Reliability of ChatGPT for Text Annotation and Classification: A Cautionary Remark](https://mlsanity.com/paper/2304.11085/) (2023)
- [Fighting Fire with Fire: Can ChatGPT Detect AI-generated Text?](https://mlsanity.com/paper/2308.01284/) (2023)
- [Practical Text Classification With Large Pre-Trained Language Models](https://mlsanity.com/paper/1812.01207/) (2018)
- [SMedBERT: A Knowledge-Enhanced Pre-trained Language Model with Structured Semantics for Medical Text Mining](https://mlsanity.com/paper/2108.08983/) (2021)
- [Cross-lingual Zero- and Few-shot Hate Speech Detection Utilising Frozen Transformer Language Models and AXEL](https://mlsanity.com/paper/2004.13850/) (2020)
- [Countering hate on social media: Large scale classification of hate and counter speech](https://mlsanity.com/paper/2006.01974/) (2020)
- [Mono vs Multilingual BERT for Hate Speech Detection and Text Classification: A Case Study in Marathi](https://mlsanity.com/paper/2204.08669/) (2022)
- [Impact and dynamics of hate and counter speech online](https://mlsanity.com/paper/2009.08392/) (2020)
- [Causality Guided Disentanglement for Cross-Platform Hate Speech Detection](https://mlsanity.com/paper/2308.02080/) (2023)
- [PEACE: Cross-Platform Hate Speech Detection- A Causality-guided Framework](https://mlsanity.com/paper/2306.08804/) (2023)
- [Towards Legally Enforceable Hate Speech Detection for Public Forums](https://mlsanity.com/paper/2305.13677/) (2023)
- [From Universal Language Model to Downstream Task: Improving RoBERTa-Based Vietnamese Hate Speech Detection](https://mlsanity.com/paper/2102.12162/) (2021)

## Built on by (most cited first, 4 of 4)

- [Toxic Memes: A Survey of Computational Perspectives on the Detection and Explanation of Meme Toxicities](https://mlsanity.com/paper/2406.07353/) (2024)
- [Are Large Language Models Good In-context Learners for Financial Sentiment Analysis?](https://mlsanity.com/paper/2503.04873/) (2025)
- [Cross-Platform Hate Speech Detection with Weakly Supervised Causal Disentanglement](https://mlsanity.com/paper/2404.11036/) (2024)
- [Online Anti-sexist Speech: Identifying Resistance to Gender Bias in Political Discourse](https://mlsanity.com/paper/2508.11434/) (2025)

## Abstract (arXiv)

Large language models (LLMs) excel in many diverse applications beyond language generation, e.g., translation, summarization, and sentiment analysis. One intriguing application is in text classification. This becomes pertinent in the realm of identifying hateful or toxic speech -- a domain fraught with challenges and ethical dilemmas. In our study, we have two objectives: firstly, to offer a literature review revolving around LLMs as classifiers, emphasizing their role in detecting and classifying hateful or toxic content. Subsequently, we explore the efficacy of several LLMs in classifying hate speech: identifying which LLMs excel in this task as well as their underlying attributes and training. Providing insight into the factors that contribute to an LLM proficiency (or lack thereof) in discerning hateful content. By combining a comprehensive literature review with an empirical analysis, our paper strives to shed light on the capabilities and constraints of LLMs in the crucial domain of hate speech detection.