The Challenge of Multilingual Toxicity in Large Language Models (LLMs)
Practical Solutions and Value
The growth of low-quality data online can lead to harmful advice or aggressive behavior in large language models (LLMs) like chatbots. This poses a risk to users. AI2 and CMU have addressed this by creating PolygloToxicityPrompts, a dataset of 425,000 prompts across 17 languages to capture multilingual toxicity.
PolygloToxicityPrompts focuses on short, potentially toxic text snippets, allowing models to identify toxicity at the early stages of communication. By leveraging this dataset, organizations can create more robust models, contribute to safer online environments, and improve proactive moderation and multilingual content filtering.
It’s important for companies to evolve with AI, stay competitive, and leverage PolygloToxicityPrompts to redefine work processes. AI can be used to locate key customer interaction points, ensure measurable impacts on business outcomes, choose customized tools, and implement AI gradually. For AI KPI management advice and insights into leveraging AI, connect with us at hello@itinai.com or follow us on Telegram or Twitter.
Discover how AI can redefine your sales processes and customer engagement. Explore solutions at itinai.com.
