arXiv AI
Toxicity Is a Property of Communicative Events: A Contextual Stress Measurement Framework for NLP
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arXiv AI
Jul 20
Conditional Reliability of Toxicity Signals for Multilingual and Code-Mixed Abuse Detection
arXiv:2607. 15861v1 Announce Type: cross Abstract: Moderation systems increasingly rely on external toxicity tools, but those tools are unreliable under code-mixing, transliteration, slang, and language mismatch.
arXiv AI
Jul 28
Beyond a Global Norm: Personalizing Toxicity Sensitivity in Language Models Without Retraining
arXiv:2607. 23175v1 Announce Type: cross Abstract: Reducing toxicity is often framed as a global alignment problem, yet perceptions of harmful language are subjective and context-dependent.
arXiv AI
Aug 18
HarmProfile: Characterizing Harmful Distributions in Frontier LLMs
arXiv:2608. 14577v1 Announce Type: cross Abstract: Frontier large language models (LLMs) safety evaluation has largely treated harmful generation as an attack outcome rather than as an object of analysis.
Hugging Face Trending Papers
Oct 5
From Abusive Language Classification to Sequence Labeling Identification
The paper introduces Abusive Language Identification (ALI), a sequence‑labeling approach that simultaneously extracts abusive spans and identifies target mentions, addressing limitations of traditional sentence‑level abusive language classification (ALC). Using a pilot corpus from a production moderation pipeline, the authors compare ALI to ALC, finding that ALI performs competitively while offering localized outputs useful for moderators, with a modest advantage on implicit abuse. However, accurately determining exact abusive boundaries and target spans remains challenging, and the study highlights the need for further research on target‑span linking and structured benchmarks for ALI.
arXiv Machine Learning
Jul 24
thaulab@EEUCA 2026: Who Said What to Whom? A Targeting-Aware Neural-Symbolic Pipeline for Gaming Toxicity Detection
arXiv:2607. 20447v1 Announce Type: cross Abstract: This paper describes our system for the EEUCA 2026 Shared Task on toxicity classification in gaming chat.
arXiv AI
Aug 25
Towards Safer Social Media Platforms: Scalable and Performant Few-Shot Harmful Content Moderation Using Large Language Models
The paper presents a scalable approach to harmful content moderation on social media by leveraging large language models (LLMs) for few-shot, in-context learning. Experiments across multiple LLMs show that this method outperforms proprietary baselines such as Perspective and OpenAI Moderation, as well as prior few-shot learning techniques, in detecting harmful content. The study also explores the addition of visual cues like video thumbnails to assess multimodal improvements, highlighting the advantages of LLM-based moderation for dynamic and large-scale content filtering.