# Generative AI for Hate Speech Detection: Evaluation and Findings arXiv: https://arxiv.org/abs/2311.09993 ยท HTML: https://mlsanity.com/paper/2311.09993/ ## Key facts - Cited by 7 papers in the map (data through 14 Oct 2026). - Builds on 29 of its 38 references that are in the map. - First posted on arXiv 16 Nov 2023. ## Builds on (most cited first, 29 of 29) - [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) - [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022) - [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019) - [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019) - [Don't Stop Pretraining: Adapt Language Models to Domains and Tasks](https://mlsanity.com/paper/2004.10964/) (2020) - [Automated Hate Speech Detection and the Problem of Offensive Language](https://mlsanity.com/paper/1703.04009/) (2017) - [ToxiGen: A Large-Scale Machine-Generated Dataset for Adversarial and Implicit Hate Speech Detection](https://mlsanity.com/paper/2203.09509/) (2022) - [HateXplain: A Benchmark Dataset for Explainable Hate Speech Detection](https://mlsanity.com/paper/2012.10289/) (2020) - [Toxicity in ChatGPT: Analyzing Persona-assigned Language Models](https://mlsanity.com/paper/2304.05335/) (2023) - [Large Scale Crowdsourcing and Characterization of Twitter Abusive Behavior](https://mlsanity.com/paper/1802.00393/) (2018) - [A Comprehensive Capability Analysis of GPT-3 and GPT-3.5 Series Models](https://mlsanity.com/paper/2303.10420/) (2023) - [HateBERT: Retraining BERT for Abusive Language Detection in English](https://mlsanity.com/paper/2010.12472/) (2020) - [Hate Speech Dataset from a White Supremacy Forum](https://mlsanity.com/paper/1809.04444/) (2018) - [Want To Reduce Labeling Cost? GPT-3 Can Help](https://mlsanity.com/paper/2108.13487/) (2021) - [Not Enough Data? Deep Learning to the Rescue!](https://mlsanity.com/paper/1911.03118/) (2019) - [Challenges in Automated Debiasing for Toxic Language Detection](https://mlsanity.com/paper/2102.00086/) (2021) - [Contextualizing Hate Speech Classifiers with Post-hoc Explanation](https://mlsanity.com/paper/2005.02439/) (2020) - [Detecting Online Hate Speech Using Context Aware Models](https://mlsanity.com/paper/1710.07395/) (2017) - [Detecting Hate Speech with GPT-3](https://mlsanity.com/paper/2103.12407/) (2021) - [Robust Hate Speech Detection in Social Media: A Cross-Dataset Empirical Evaluation](https://mlsanity.com/paper/2307.01680/) (2023) - [Data Expansion using Back Translation and Paraphrasing for Hate Speech Detection](https://mlsanity.com/paper/2106.04681/) (2021) - [Fight Fire with Fire: Fine-tuning Hate Detectors using Large Samples of Generated Hate Speech](https://mlsanity.com/paper/2109.00591/) (2021) - [Towards Hate Speech Detection at Large via Deep Generative Modeling](https://mlsanity.com/paper/2005.06370/) (2020) - [Assessing the impact of contextual information in hate speech detection](https://mlsanity.com/paper/2210.00465/) (2022) - [Character-level HyperNetworks for Hate Speech Detection](https://mlsanity.com/paper/2111.06336/) (2021) - [CRUSH: Contextually Regularized and User anchored Self-supervised Hate speech Detection](https://mlsanity.com/paper/2204.06389/) (2022) - [Detecting Multidimensional Political Incivility on Social Media](https://mlsanity.com/paper/2305.14964/) (2023) - [Social World Knowledge: Modeling and Applications](https://mlsanity.com/paper/2306.16299/) (2023) ## Built on by (most cited first, 7 of 7) - [What Does the Bot Say? Opportunities and Risks of Large Language Models in Social Media Bot Detection](https://mlsanity.com/paper/2402.00371/) (2024) - [Guardians and Offenders: A Survey on Harmful Content Generation and Safety Mitigation of LLM](https://mlsanity.com/paper/2508.05775/) (2025) - [Re-examining Sexism and Misogyny Classification with Annotator Attitudes](https://mlsanity.com/paper/2410.03543/) (2024) - [HateTinyLLM : Hate Speech Detection Using Tiny Large Language Models](https://mlsanity.com/paper/2405.01577/) (2024) - [Watching the Watchers: A Comparative Fairness Audit of Cloud-based Content Moderation Services](https://mlsanity.com/paper/2406.14154/) (2024) - [Evolving Hate Speech Online: An Adaptive Framework for Detection and Mitigation](https://mlsanity.com/paper/2502.10921/) (2025) - [A Peek Behind the Curtain: Using Step-Around Prompt Engineering to Identify Bias and Misinformation in GenAI Models](https://mlsanity.com/paper/2503.15205/) (2025) ## Abstract (arXiv) Automatic hate speech detection using deep neural models is hampered by the scarcity of labeled datasets, leading to poor generalization. To mitigate this problem, generative AI has been utilized to generate large amounts of synthetic hate speech sequences from available labeled examples, leveraging the generated data in finetuning large pre-trained language models (LLMs). In this chapter, we provide a review of relevant methods, experimental setups and evaluation of this approach. In addition to general LLMs, such as BERT, RoBERTa and ALBERT, we apply and evaluate the impact of train set augmentation with generated data using LLMs that have been already adapted for hate detection, including RoBERTa-Toxicity, HateBERT, HateXplain, ToxDect, and ToxiGen. An empirical study corroborates our previous findings, showing that this approach improves hate speech generalization, boosting recall performance across data distributions. In addition, we explore and compare the performance of the finetuned LLMs with zero-shot hate detection using a GPT-3.5 model. Our results demonstrate that while better generalization is achieved using the GPT-3.5 model, it achieves mediocre recall and low precision on most datasets. It is an open question whether the sensitivity of models such as GPT-3.5, and onward, can be improved using similar techniques of text generation.