Fine-Tuning Llama 2 Large Language Models for Detecting Online Sexual
  Predatory Chats and Abusive Texts
Thanh Thi Nguyen Campbell Wilson Janis Dalins
Detecting online sexual predatory behaviours and abusive language 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. Researchers have been exploring various techniques
and approaches to develop effective detection systems that can identify and
mitigate these risks. Recent development of large language models (LLMs) has
opened a new opportunity to address this problem more effectively. This paper
proposes an approach to detection of online sexual predatory chats and abusive
language using the open-source pretrained Llama 2 7B-parameter model, recently
released by Meta GenAI. We fine-tune the LLM using datasets with different
sizes, imbalance degrees, and languages (i.e., English, Roman Urdu and Urdu).
Based on the power of LLMs, our approach is generic and automated without a
manual search for a synergy between feature extraction and classifier design
steps like conventional methods in this domain. Experimental results show a
strong performance of the proposed approach, which performs proficiently and
consistently across three distinct datasets with five sets of experiments. 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 solving text classification
problems with other potential applications such as sentiment analysis, spam and
phishing detection, sorting legal documents, fake news detection, language
identification, user intent recognition, text-based product categorization,
medical record analysis, and resume screening.