Francielle Vargas, Jackson Trager, Diego Alves, Matteo Guida, Surendrabikram Thapa, Berk Atıl, Daryna Dementieva, Andrew J Smart, Ameeta Agrawal
| Challenge: | Existing models for hate speech detection are opaque and rely on surface-level cues. Existing approaches often encode biases originating from training data and annotation processes. |
| Approach: | They propose a framework that integrates moral rationale supervision into training . they propose SMRA for self-explaining hate speech detection . |
| Outcome: | The proposed framework improves performance across binary hate speech detection and multi-label moral sentiment classification. |
Similar Papers
HateBRXplain: A Benchmark Dataset with Human-Annotated Rationales for Explainable Hate Speech Detection in Brazilian Portuguese (2025.coling-main)
Copied to clipboard
| Challenge: | Hate speech detection systems have been developed to inhibit offensive and hateful language from being published or spread on the Web and social media. |
| Approach: | They propose to use a Portuguese dataset to provide rationales for hate speech detection with text span annotations. |
| Outcome: | The proposed models outperform the baselines in Portuguese and showed that they provide plausible explanations when compared to human annotations. |
HARE: Explainable Hate Speech Detection with Step-by-Step Reasoning (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Recent benchmarks have attempted to identify and explain hate speech but lack the reasoning to supervise detection models. |
| Approach: | They propose a framework that uses large language models to fill in the gaps in hate speech explanations by using existing annotations. |
| Outcome: | The proposed framework outperforms baselines on SBIC and Implicit Hate using model-generated data and improves generalization to unseen datasets. |
MFTCXplain: A Multilingual Benchmark Dataset for Evaluating the Moral Reasoning of LLMs through Multi-hop Hate Speech Explanation (2025.findings-emnlp)
Copied to clipboard
Jackson Trager, Francielle Vargas, Diego Alves, Matteo Guida, Mikel K. Ngueajio, Ameeta Agrawal, Yalda Daryani, Farzan Karimi Malekabadi, Flor Miriam Plaza-del-Arco
| Challenge: | Existing evaluation benchmarks for large language models lack annotations that justify moral classifications and focus on English constrain moral reasoning across diverse cultural settings. |
| Approach: | They propose a multilingual benchmark dataset for evaluating moral reasoning of large language models . it includes 3,000 tweets annotated with binary hate speech labels, moral categories and rationales . |
| Outcome: | The proposed dataset shows a misalignment between LLM outputs and human annotations in moral reasoning tasks. |
Mitigating Biases in Hate Speech Detection from A Causal Perspective (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods to detect hate speech are prone to spurious correlations between training data and labels, which could lead to biased treatment of vulnerable and minority groups. |
| Approach: | They propose to use grammar induction to find grammar patterns for hate speech and analyze this phenomenon from a causal perspective. |
| Outcome: | The proposed methods can detect hate speech from a causal perspective and are robust to different datasets. |
HateXScore: A Metric Suite for Evaluating Reasoning Quality in Hate Speech Explanations (2026.eacl-long)
Copied to clipboard
| Challenge: | Existing evaluation frameworks do not assess why a text is deemed hateful . authors present a new metric to evaluate the reasoning quality of model explanations . |
| Approach: | They propose a metric suite to evaluate the reasoning quality of model explanations. |
| Outcome: | The proposed metric validates it as a practical tool for trustworthy and transparent moderation on six diverse hate speech datasets. |
Why Is It Hate Speech? Masked Rationale Prediction for Explainable Hate Speech Detection (2022.coling-1)
Copied to clipboard
| Challenge: | Hate speech cannot be identified based solely on the presence of specific words; model should reason like humans and be explainable. |
| Approach: | They propose to use Masked Rationale Prediction to predict masked human rationales . the method performs hate speech detection robustly in terms of bias and explainability . |
| Outcome: | The proposed method performs state-of-the-art in terms of bias and explainability. |
Towards Explainable Hate Speech Detection (2025.findings-acl)
Copied to clipboard
| Challenge: | Recent advances in deep learning have significantly enhanced the efficiency and accuracy of natural language processing (NLP) tasks. |
| Approach: | They propose a model that uses valence, arousal, and dominance (VAD) scores to detect hate speech and a weighted sum of valent, valance, and valency (VA) scores for classification. |
| Outcome: | The proposed model can compete with state-of-the-art models in detecting hate speech and non-hate speech words based on their individual and summed VAD-values. |
LEAF: Towards Lightweight Explainable Hateful Video Detection via Self-Grounding CoT Guided Stage-Wise Distillation (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for detecting hateful videos rely on opaque models with no insight into their decisions. |
| Approach: | They propose a lightweight, explainable video detection framework that distills "explainability" from LMMs into efficient Smaller Multimodal Models (SMMs) they use a self-grounded chain-of-thought mechanism to generate unbiased supervision signals for videos . |
| Outcome: | The proposed framework outperforms existing methods in detection accuracy and explainability on three video benchmarks. |
Explain the Flag: Contextualizing Hate Speech Beyond Censorship (2026.findings-acl)
Copied to clipboard
Jason Liartis, Eirini Kaldeli, Lamprini Gyftokosta, Eleftherios Chelioudakis, Orfeas Menis Mastromichalakis
| Challenge: | a hybrid approach to detect and explain hate speech combines large language models with vocabularies to detect hate speech in three languages . authors: the spread of hate speech online has serious personal, social, and legal consequences . eu has launched initiatives to analyze, regulate, and counteract online hate speech, authors say . |
| Approach: | They propose a hybrid approach that combines Large Language Models with vocabularies to detect hate speech in English, French, and Greek. |
| Outcome: | The proposed approach outperforms baselines in English, French, and Greek . it uses large language models and vocabularies to detect and explain hate speech . human evaluation shows that the proposed approach is accurate and clear . |
HARM: Learning Hate-Aware Reward Model for Evaluating Natural Language Explanations of Offensive Content (2026.findings-eacl)
Copied to clipboard
| Challenge: | Existing reward models for explaining hate speech are optimized for broad notions of safety, but they assign lower scores to contextually rich explanations. |
| Approach: | They propose a reward model that integrates interpretable signals to better align reward scores with the needs of hate speech explanation. |
| Outcome: | The proposed model outperforms general-purpose baselines and improves pair-wise preference. |