A Study on the Application of Large Language Models Based on LoRA Fine-Tuning and Difficult-Sample Adaptation for Online Violence Recognition
Zhengguang Gao, Shenjia Jing, Lihong Zhang · Symmetry · 2025
This study introduces the concept of symmetry as a fundamental theoretical perspective for understanding the linguistic structure of cyberbullying texts. It posits that such texts often exhibit symmetry breaking between surface-level language forms and underlying semantic intent. This structural-semantic asymmetry increases the complexity of the recognition task and places higher demands on the semantic modeling capabilities of detection systems. With the rapid growth of social media, the covert and harmful nature of cyberbullying speech has become increasingly prominent, posing serious challenges to public opinion management and public safety. While mainstream approaches to cyberbullying detection—typically based on traditional deep learning models or pre-trained language models—have achieved some progress, they still struggle with low accuracy, poor generalization, and weak interpretability when handling implicit, semantically complex, or borderline expressions. To address these challenges, this paper proposes a cyberbullying detection method that combines LoRA-based fine-tuning with Small-Scale Hard-Sample Adaptive Training (S-HAT), leveraging a large language model framework based on Meta-Llama-3-8B-Instruct. The method employs prompt-based techniques to identify inference failures and integrates model-generated reasoning paths for lightweight fine-tuning. This enhances the model’s ability to capture and represent semantic asymmetry in cyberbullying texts. Experiments conducted on the ToxiCN dataset demonstrate that the S-HAT approach achieves a precision of 84.1% using only 24 hard samples—significantly outperforming baseline models such as BERT and RoBERTa. The proposed method not only improves recognition accuracy but also enhances model interpretability and deployment efficiency, offering a practical and intelligent solution for cyberbullying mitigation.