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Hugging Face Trending Papers

Introducing the Privacy-HSD Trade-off: Hate Speech Detection, but not at the Cost of Privacy

The paper introduces the privacy‑HSD trade‑off, highlighting that automatic hate speech detection systems can inadvertently compromise user privacy by encoding authorship. It demonstrates that such systems may achieve high performance at the expense of privacy, and proposes a new domain‑specific technique, AgnoSpeech, alongside other text privatization methods to balance these competing goals. The authors benchmark these methods, showing that while challenging, it is feasible to protect privacy without sacrificing hate‑speech detection effectiveness.

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arXiv Computation and Language
Sep 3

The Enforcement and Feasibility of Hate Speech Moderation

The study audits hate‑speech moderation on Twitter (now X) using 540,000 annotated tweets from a full day. Eighty percent of hateful tweets, including violent content, remained online after five months, and removal was only slightly more likely than for non‑hateful tweets, far below the rates for scams or adult content. Automated detection could not reliably classify hate but ranked it highly, allowing human triage; however, current staffing curbed little exposure, while substantial reductions were financially feasible and far below applicable regulatory fines.

By Manuel Tonneau, Dylan Thurgood, Diyi Liu, Niyati Malhotra, Victor Orozco-Olvera, Ralph Schroeder, Scott A. Hale, Manoel Horta Ribeiro, Paul R\"ottger, Samuel P. Fraiberger
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arXiv Machine Learning
Jul 30

DP-MGTD: Privacy-Preserving Machine-Generated Text Detection via Adaptive Differentially Private Entity Sanitization

arXiv:2601. 04641v2 Announce Type: replace-cross Abstract: The deployment of Machine-Generated Text (MGT) detection systems necessitates processing sensitive user data, creating a fundamental conflict between authorship verification and privacy preservation.

By Lionel Z. Wang, Yusheng Zhao, Jiabin Luo, Xinfeng Li, Lixu Wang, Yinan Peng, Haoyang Li, XiaoFeng Wang, Wei Dong
safety
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arXiv Computation and Language
Sep 14

I Am No One: Style-Aware Paraphrasing for Text Anonymization

The paper introduces a style-aware paraphrasing method for text anonymization that leverages pretrained large language models to build compact stylistic profiles from minimal samples and rewrite text to suppress identifiable style markers while preserving meaning. It demonstrates that this approach reduces authorship attribution F1 scores by 60‑70% on blog and review datasets, outperforming both differential privacy‑based and non‑DP baselines, and maintains content quality and readability.

By Ahmed Sohair Khan, Estrid He, Monica Wachowicz, Elham Naghizade
llmsmultimodalsafety
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arXiv AI
Sep 4

Anonymization, Not Elimination: Utility-Preserved Speech Anonymization

The paper introduces a two‑stage speech anonymization framework that preserves both linguistic content and acoustic identity. It replaces personally identifiable information using a generative editing model and applies a flow‑matching anonymization technique (F3‑VA) to create diverse, distinct anonymized speakers. The authors evaluate privacy with speaker verification metrics and utility by training ASR, TTS, and SER models from scratch, showing stronger privacy protection with minimal utility loss compared to existing baselines.

By Yunchong Xiao, Yuxiang Zhao, Ziyang Ma, Shuai Wang, Kai Yu, Jiachun Liao, Xie Chen
multimodalsafety
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arXiv Computation and Language
Aug 27

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.

By Lukas Edman, Daryna Dementieva, Alexander Fraser
llmsnlpfine-tuningbenchmarks
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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
llms
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