PSK@EEUCA 2026: Fine-tuning Large Language Models with Synthetic Data Augmentation for Multi-class Toxicity Detection in Gaming Chat
Year: 2026
Type: conference-paper
Abstract: This paper describes our system for the EEUCA 2026 Shared Task on Understanding Toxic Behavior in Gaming Communities.The task involves classifying World of Tanks chat messages into six toxicity categories: Non-toxic, Insults/Flaming, Other Offensive, Hate/Harassment, Threats, and Extremism.We explore multiple approaches including encoderbased models, instruction-tuned LLMs with LoRA fine-tuning, hierarchical classification, one-vs-rest strategies, and various ensemble methods.Our best system combines Llama 3.1 8B with carefully calibrated 5% synthetic data augmentation, achieving an F1-macro score of 0.6234 on the test set, placing 4th out of 35 participating teams.We provide extensive analysis of the dataset's annotation patterns and their impact on model generalization, revealing a critical "validation trap" phenomenon where high validation performance correlates with poor test transfer.
Author: Srikar Kashyap Pulipaka
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166858647