Fine-Tuning Llama 2 Large Language Models for Detecting Online Sexual Predatory Chats and Abusive Texts
Thanh Thi Nguyen, Campbell Wilson, Janis Dalins
Abstract
This paper proposes an approach to detection of online harmful content using the open-source pretrained Llama 2 model, recently released by Meta GenAI.We fine-tune the LLM using datasets with different sizes, imbalance degrees, and languages.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.Experimental results show a strong performance of the proposed approach, which is proficient and consistent 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, and hate speech in online discussions and comments to maintain respectful digital communities.