# HARE: Explainable Hate Speech Detection with Step-by-Step Reasoning

arXiv: https://arxiv.org/abs/2311.00321 · HTML: https://mlsanity.com/paper/2311.00321/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- Builds on 34 of its 48 references that are in the map.
- First posted on arXiv 1 Nov 2023.

## Builds on (most cited first, 30 of 34)

- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [Self-Consistency Improves Chain of Thought Reasoning in Language Models](https://mlsanity.com/paper/2203.11171/) (2022)
- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [Finetuned Language Models Are Zero-Shot Learners](https://mlsanity.com/paper/2109.01652/) (2021)
- [CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge](https://mlsanity.com/paper/1811.00937/) (2018)
- [Adafactor: Adaptive Learning Rates with Sublinear Memory Cost](https://mlsanity.com/paper/1804.04235/) (2018)
- [HateXplain: A Benchmark Dataset for Explainable Hate Speech Detection](https://mlsanity.com/paper/2012.10289/) (2020)
- [Social Bias Frames: Reasoning about Social and Power Implications of Language](https://mlsanity.com/paper/1911.03891/) (2019)
- [Large Language Models Are Reasoning Teachers](https://mlsanity.com/paper/2212.10071/) (2022)
- [Can language models learn from explanations in context?](https://mlsanity.com/paper/2204.02329/) (2022)
- [Learning from the Worst: Dynamically Generated Datasets to Improve Online Hate Detection](https://mlsanity.com/paper/2012.15761/) (2020)
- [Latent Hatred: A Benchmark for Understanding Implicit Hate Speech](https://mlsanity.com/paper/2109.05322/) (2021)
- [Racial Bias in Hate Speech and Abusive Language Detection Datasets](https://mlsanity.com/paper/1905.12516/) (2019)
- [HateBERT: Retraining BERT for Abusive Language Detection in English](https://mlsanity.com/paper/2010.12472/) (2020)
- [FLASK: Fine-grained Language Model Evaluation based on Alignment Skill Sets](https://mlsanity.com/paper/2307.10928/) (2023)
- [Measuring Association Between Labels and Free-Text Rationales](https://mlsanity.com/paper/2010.12762/) (2020)
- [Reframing Human-AI Collaboration for Generating Free-Text Explanations](https://mlsanity.com/paper/2112.08674/) (2021)
- [Understanding Abuse: A Typology of Abusive Language Detection Subtasks](https://mlsanity.com/paper/1705.09899/) (2017)
- [SCOTT: Self-Consistent Chain-of-Thought Distillation](https://mlsanity.com/paper/2305.01879/) (2023)
- [Hate Speech Detection and Racial Bias Mitigation in Social Media based on BERT model](https://mlsanity.com/paper/2008.06460/) (2020)
- [Few-Shot Self-Rationalization with Natural Language Prompts](https://mlsanity.com/paper/2111.08284/) (2021)
- [Do LLMs Understand Social Knowledge? Evaluating the Sociability of Large Language Models with SocKET Benchmark](https://mlsanity.com/paper/2305.14938/) (2023)
- [A Systematic Investigation of Commonsense Knowledge in Large Language Models](https://mlsanity.com/paper/2111.00607/) (2021)
- [PINTO: Faithful Language Reasoning Using Prompt-Generated Rationales](https://mlsanity.com/paper/2211.01562/) (2022)
- [Characterizing and Detecting Hateful Users on Twitter](https://mlsanity.com/paper/1803.08977/) (2018)
- [Towards Explainable NLP: A Generative Explanation Framework for Text Classification](https://mlsanity.com/paper/1811.00196/) (2018)
- [A Just and Comprehensive Strategy for Using NLP to Address Online Abuse](https://mlsanity.com/paper/1906.01738/) (2019)
- [FBERT: A Neural Transformer for Identifying Offensive Content](https://mlsanity.com/paper/2109.05074/) (2021)

## Built on by (most cited first, 30 of 31)

- [Recent Advances in Hate Speech Moderation: Multimodality and the Role of Large Models](https://mlsanity.com/paper/2401.16727/) (2024)
- [Hate Personified: Investigating the role of LLMs in content moderation](https://mlsanity.com/paper/2410.02657/) (2024)
- [Explainability in Practice: A Survey of Explainable NLP Across Various Domains](https://mlsanity.com/paper/2502.00837/) (2025)
- [MemeIntel: Explainable Detection of Propagandistic and Hateful Memes](https://mlsanity.com/paper/2502.16612/) (2025)
- [ExPO-HM: Learning to Explain-then-Detect for Hateful Meme Detection](https://mlsanity.com/paper/2510.08630/) (2025)
- [Hate Speech Detection with Generalizable Target-aware Fairness](https://mlsanity.com/paper/2406.00046/) (2024)
- [A Modular Taxonomy for Hate Speech Definitions and Its Impact on Zero-Shot LLM Classification Performance](https://mlsanity.com/paper/2506.18576/) (2025)
- [LLM-Based Multi-Task Bangla Hate Speech Detection: Type, Severity, and Target](https://mlsanity.com/paper/2510.01995/) (2025)
- [HateCOT: An Explanation-Enhanced Dataset for Generalizable Offensive Speech Detection via Large Language Models](https://mlsanity.com/paper/2403.11456/) (2024)
- [Guardians of Discourse: Evaluating LLMs on Multilingual Offensive Language Detection](https://mlsanity.com/paper/2410.15623/) (2024)
- [Towards Efficient and Explainable Hate Speech Detection via Model Distillation](https://mlsanity.com/paper/2412.13698/) (2024)
- [Enhancing LLM-based Hatred and Toxicity Detection with Meta-Toxic Knowledge Graph](https://mlsanity.com/paper/2412.15268/) (2024)
- [LLM-C3MOD: A Human-LLM Collaborative System for Cross-Cultural Hate Speech Moderation](https://mlsanity.com/paper/2503.07237/) (2025)
- [Eagle: Ethical Dataset Given from Real Interactions](https://mlsanity.com/paper/2402.14258/) (2024)
- [Evaluation of Hate Speech Detection Using Large Language Models and Geographical Contextualization](https://mlsanity.com/paper/2502.19612/) (2025)
- [Aligning Attention with Human Rationales for Self-Explaining Hate Speech Detection](https://mlsanity.com/paper/2511.07065/) (2025)
- [Argument-Based Consistency in Toxicity Explanations of LLMs](https://mlsanity.com/paper/2506.19113/) (2025)
- [CFSafety: Comprehensive Fine-grained Safety Assessment for LLMs](https://mlsanity.com/paper/2410.21695/) (2024)
- [Specializing General-purpose LLM Embeddings for Implicit Hate Speech Detection across Datasets](https://mlsanity.com/paper/2508.20750/) (2025)
- [Are LLMs Enough for Hyperpartisan, Fake, Polarized and Harmful Content Detection? Evaluating In-Context Learning vs. Fine-Tuning](https://mlsanity.com/paper/2509.07768/) (2025)
- [Fine-Tuning Large Language Models with QLoRA for Offensive Language Detection in Roman Urdu-English Code-Mixed Text](https://mlsanity.com/paper/2510.03683/) (2025)
- [HateMirage: An Explainable Multi-Dimensional Dataset for Decoding Faux Hate and Subtle Online Abuse](https://mlsanity.com/paper/2603.02684/) (2026)
- [Large Language Models in the Abuse Detection Pipeline](https://mlsanity.com/paper/2604.00323/) (2026)
- [Selective Demonstration Retrieval for Improved Implicit Hate Speech Detection](https://mlsanity.com/paper/2504.12082/) (2025)
- [Classification is a RAG problem: A case study on hate speech detection](https://mlsanity.com/paper/2508.06204/) (2025)
- [Decoding Multimodal Cues: Unveiling the Implicit Meaning Behind Hateful Videos](https://mlsanity.com/paper/2606.11953/) (2026)
- [CLARA: Clip-Level Multimodal Alignment with VLM-Derived Rationales for Hateful Video Detection](https://mlsanity.com/paper/2608.15905/) (2026)
- [Introducing the Privacy-HSD Trade-off: Hate Speech Detection, but not at the Cost of Privacy](https://mlsanity.com/paper/2608.19006/) (2026)
- [SPAR-Hate: Auditor-Guided Multi-Perspective Role Reasoning for Bilingual Hate Speech Parsing](https://mlsanity.com/paper/2608.22018/) (2026)
- [Sledgehammer or Scalpel? A Fine-grained Adaptive Framework for Implicit Hate Speech](https://mlsanity.com/paper/2608.27462/) (2026)

## Abstract (arXiv)

With the proliferation of social media, accurate detection of hate speech has become critical to ensure safety online. To combat nuanced forms of hate speech, it is important to identify and thoroughly explain hate speech to help users understand its harmful effects. Recent benchmarks have attempted to tackle this issue by training generative models on free-text annotations of implications in hateful text. However, we find significant reasoning gaps in the existing annotations schemes, which may hinder the supervision of detection models. In this paper, we introduce a hate speech detection framework, HARE, which harnesses the reasoning capabilities of large language models (LLMs) to fill these gaps in explanations of hate speech, thus enabling effective supervision of detection models. Experiments on SBIC and Implicit Hate benchmarks show that our method, using model-generated data, consistently outperforms baselines, using existing free-text human annotations. Analysis demonstrates that our method enhances the explanation quality of trained models and improves generalization to unseen datasets. Our code is available at https://github.com/joonkeekim/hare-hate-speech.git.