arXiv AI
SPAR-Hate: An Auditor-Guided Multi-Agent Framework for Bilingual Hate Speech Parsing
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arXiv AI
4d ago
SPAR-Hate: Auditor-Guided Multi-Perspective Role Reasoning for Bilingual Hate Speech Parsing
arXiv:2608.22018v2 Announce Type: replace Abstract: Hate speech research has moved from coarse-grained classification towards structured parsing, where systems jointly identify targets, supporting ar...
arXiv AI
2d ago
Training-Time Explainability for Multilingual Hate Speech Detection: Aligning Model Reasoning with Human Rationales
The paper proposes a training‑time explainability framework that aligns model reasoning with human‑annotated rationales to improve both classification performance and interpretability for multilingual hate speech detection. It is evaluated on HateXplain (English) and BullySent (Hinglish), datasets that capture anti‑Muslim hate in culturally coded, multilingual forms. Using methods such as LIME, Integrated Gradients, Grad‑X‑Input, and attention, the study shows that gradient‑ and attention‑based regularization boosts F‑scores, enhances plausibility and faithfulness, and captures culturally specific cues for detecting implicit anti‑Muslim hate.
Hugging Face Trending Papers
3d ago
From Specialization to Generalization: Instruction-tuned LLMs for Robust Harmful Content Mitigation
Large language models (LLMs) demonstrate impressive performance across a wide range of general NLP tasks; however, their effectiveness in sensitive domains, such as hate speech detection, remains less...
arXiv Computation and Language
3d ago
From Specialization to Generalization: Instruction-tuned LLMs for Robust Harmful Content Mitigation
The paper presents an instruction‑tuned large language model (LLM) based on Qwen3 that is fine‑tuned for hate speech mitigation by unifying 36 English hate speech datasets. The authors show that this generalist LLM achieves state‑of‑the‑art performance on in‑domain benchmarks and delivers significant gains in cross‑domain and cross‑lingual generalization, outperforming specialist encoder‑based classifiers.
arXiv AI
Jun 9
Dealing with Annotator Disagreement in Hate Speech Classification
arXiv:2502. 08266v3 Announce Type: replace-cross Abstract: Hate speech detection is a crucial task, especially on social media where harmful content can spread quickly.
arXiv AI
Aug 20
Efficient Adaptation of LLMs for Hate Speech Detection in Low-Resource Languages: A Comparative Study on Roman Urdu
The paper evaluates large language models for hate‑speech detection in Roman Urdu, a low‑resource language with informal spelling variations. Using the Parameter‑Efficient Fine‑Tuning technique Low‑Rank Adaptation (LoRA), the authors fine‑tune models such as Mistral, LLaMA, Falcon, and multilingual BERT on the 72,000‑comment PURUTT dataset. While zero‑shot performance yields an F1 of 0.56, fine‑tuning a small fraction of parameters boosts F1 scores above 0.93, demonstrating that PEFT offers both high accuracy and computational efficiency for low‑resource language tasks.