AISPACE at SemEval-2024 task 8: A Class-balanced Soft-voting System for
  Detecting Multi-generator Machine-generated Text
Renhua Gu Xiangfeng Meng
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.