arXiv Computation and Language By Han Wang, Hanqing Tao, Xuesong Wang, Yi Zhu

Sledgehammer or Scalpel? A Fine-grained Adaptive Framework for Implicit Hate Speech Detection

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The paper introduces FAID, a fine‑grained adaptive framework for detecting implicit hate speech. It first classifies samples into Shallow, Targeted, or Context‑Dependent categories and then applies tailored strategies—Prompt‑Tuning for Shallow, knowledge augmentation for Targeted, and an agentic prompt‑generation system for Context‑Dependent cases. Experiments on four benchmark datasets show that FAID outperforms state‑of‑the‑art baselines.

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arXiv Computation and Language
Aug 31

Sledgehammer or Scalpel? A Fine-grained Adaptive Framework for Implicit Hate Speech

The paper introduces FAID, a fine‑grained adaptive framework for detecting implicit hate speech. It first classifies samples into Shallow, Targeted, or Context‑Dependent categories and then applies tailored strategies—prompt‑tuning for shallow cases, knowledge augmentation for targeted ones, and an agentic prompt‑generation system for context‑dependent posts. Experiments on four benchmark datasets show that FAID outperforms state‑of‑the‑art baselines by allocating computational effort only where needed.

By Han Wang, Yuhu Cheng, Xuesong Wang, Yi Zhu
arXiv Computation and Language
Sep 1

When Hate Meets Facts: LLMs-in-the-Loop for Check-worthiness Detection in Hate Speech

The paper introduces WSF-ARG+, a new dataset that pairs hate speech with check‑worthiness annotations, and presents an LLM‑in‑the‑loop framework to streamline the annotation process. Experiments with 12 open‑weight large language models demonstrate that the framework cuts human effort while maintaining annotation quality. The study also shows that incorporating check‑worthiness labels improves hate‑speech detection performance, boosting macro‑F1 scores for large models by up to 0.213 and averaging 0.154 across models.

By Nicol\'as Benjam\'in Ocampo, Tommaso Caselli, Davide Ceolin