arXiv:2606.27981v1 Announce Type: new Abstract: We introduce a new, contextual, multilingual dataset called ToxiREX: Toxic REasoning in ConteXt. The dataset consists of threads of Reddit comments and structured characterizations of what the comments imply, following a systematic toxic reasoning schema developed in a previous paper. Using the schema allows us to capture and explain implicit and context-dependent toxicity, while supporting mappings to existing toxicity taxonomies. The dataset includes comments in six languages (English, Arabic, Turkish, Spanish, German, and Dutch), collected fro
The proliferation of AI-generated content and the increasing scale of multilingual online interactions necessitates more robust systems for identifying and mitigating toxic reasoning.
Sophisticated detection of implicit and contextual toxicity, especially across multiple languages, is crucial for improving safety, reliability, and regulatory compliance of large language models and online platforms.
The availability of ToxiREX enables more nuanced AI training and evaluation regarding toxic reasoning, moving beyond simple keyword spotting to understanding underlying intent and context.
- · AI ethicists
- · Social media platforms
- · Multilingual content moderation services
- · Researchers in NLP and AI safety
- · Platforms with weak content moderation
- · Creators of 'jailbreaking' techniques for LLMs
AI models will become more adept at identifying and filtering subtle forms of toxicity in user-generated content.
Improved toxicity detection can lead to safer online environments and reduced spread of harmful narratives, potentially impacting online political discourse.
The ability to systematically categorize toxic reasoning might inform new regulatory frameworks or industry standards for AI safety and platform accountability.
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