AISPACE at SemEval-2024 task 8: A Class-balanced Soft-voting System for Detecting Multi-generator Machine-generated Text
Renhua Gu, Xiangfeng Meng · 2024
SemEval-2024 Task 8 provides a challenge to detect human-written and machine-generated text.There are 3 subtasks for different detection scenarios.This paper proposes a system that mainly deals with Subtask B. It aims to detect if given full text is written by human or is generated by a specific Large Language Model (LLM), which is actually a multi-class text classification task.Our team AISPACE conducted a systematic study of fine-tuning transformer-based models, including encoderonly, decoder-only and encoder-decoder models.We compared their performance on this task and identified that encoder-only models performed exceptionally well.We also applied a weighted Cross Entropy loss function to address the issue of data imbalance of different class samples.Additionally, we employed softvoting strategy over multi-models ensemble to enhance the reliability of our predictions.Our system ranked top 1 in Subtask B, which sets a state-of-the-art benchmark for this new challenge.