Detection of online harmful content using Llama 2 large language models

Speaker: Thanh Thi Nguyen

Abstract:

Detecting online harmful content on social media platforms has become a critical area of research due to the growing concerns about online safety, especially for vulnerable populations such as children and adolescents. This talk presents an approach to detection of online harmful content, specifically abusive language and sexual predatory behaviours, using the open-source pretrained Llama 2 7B-parameter model, released by Meta GenAI. We fine-tune the LLM using datasets with different sizes, imbalance degrees, and languages. This study’s outcomes indicate that the proposed method can be implemented in real-world applications (even with non-English languages) for flagging sexual predators, offensive or toxic content, hate speech, and discriminatory language in online discussions and comments to maintain respectful internet or digital communities. Furthermore, it can be employed for other problems such as sentiment analysis, spam and phishing detection, sorting legal documents, fake news detection, language identification, text-based product categorization, medical record analysis, and resume screening.

Bio:

Dr. Nguyen is an associate professor at the Ai for Law Enforcement and Community Safety (AiLECS) lab at Monash University. He was a Visiting Scholar with the Computer Science Department at Stanford University in 2015 and the Edge Computing Lab at Harvard University in 2019. He received a European-Pacific Partnership for ICT Expert Exchange Program Award from the European Commission in 2018, and an Australia–India Strategic Research Fund Early- and Mid-Career Fellowship from the Australian Academy of Science in 2020. He obtained a PhD in Mathematics and Statistics from Monash University, Australia and has expertise in artificial intelligence, reinforcement learning, NLP, computer vision, and cybersecurity.