With the multiplication of social media platforms, which offer anonymity, easy access and
online community formation and online debate, the issue of hate speech detection and …

Online toxic discourses could result in conflicts between groups or harm to online
communities. Hate speech is complex and multifaceted harmful or offensive content …

We introduce Llama Guard, an LLM-based input-output safeguard model geared towards
Human-AI conversation use cases. Our model incorporates a safety risk taxonomy, a …

Recent studies have alarmed that many online hate speeches are implicit. With its subtle
nature, the explainability of the detection of such hateful speech has been a challenging …

Sentiment analysis (SA) has been a long-standing research area in natural language
processing. It can offer rich insights into human sentiments and opinions and has thus seen …

Despite its importance, the time variable has been largely neglected in the NLP and
language model literature. In this paper, we present TimeLMs, a set of language models …

The experimental landscape in natural language processing for social media is too
fragmented. Each year, new shared tasks and datasets are proposed, ranging from classics …

Hate speech is a challenging issue plaguing the online social media. While better models
for hate speech detection are continuously being developed, there is little research on the …

On the world wide web, toxic content detectors are a crucial line of defense against
potentially hateful and offensive messages. As such, building highly effective classifiers that …

Language models are ubiquitous in current NLP, and their multilingual capacity has recently
attracted considerable attention. However, current analyses have almost exclusively focused …