Taxonomy of Indirect Linguistic Encoding for LLM-Based Coded Language Detection

A revised arXiv preprint proposes a mechanism-oriented taxonomy of indirect linguistic expressions, the disguised phrasing such as algospeak and euphemisms that users adopt to hide sensitive meaning from platforms. The work organizes these encoding strategies by how they work rather than how they look, aiming to give LLM-based detection systems a more general basis for spotting obfuscated content. It targets the gap between surface-form moderation filters and adversarial evasion in social media text.

Speech Act Features Improve Low-Data Forecasting of Online Conversation Derailment

A revised research paper describes a technique for predicting when online discussions are likely to escalate into hostility before it happens, allowing moderators to intervene early. The method draws on speech act signals and is designed to perform well with limited training data while generalizing across different topic domains. The work is published as an updated version on arXiv.

Hugging Face urges AI safety systems to refuse harmful subsets, not entire topics

A Hugging Face blog post examines how moderation classifiers and language models often block benign requests simply because they touch a flagged subject. The authors argue that refusal policies should be scoped to the genuinely harmful portion of a topic, and question whose definition of safety gets encoded into today's systems. The piece advocates building more granular safety taxonomies that cut down over-refusal without weakening protection.

WHY IT MATTERS ↘Over-refusal quietly erodes product utility and user trust while inflating eval and support costs, so teams tuning moderation stacks face a concrete trade-off between safety coverage and usability rather than a simple safety-maximizing default. The governance angle — whose definition of harm gets encoded into classifiers — also pressures vendors to document and defend their safety taxonomies as enterprises and regulators scrutinize automated content decisions.