AISPACE at SemEval-2024 task 8: A Class-balanced Soft-voting System for Detecting Multi-generator Machine-generated Text
<|reference_start|>AISPACE at SemEval-2024 task 8: A Class-balanced Soft-voting System for Detecting Multi-generator Machine-generated Text: 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.<|reference_end|>
@article{gu2024aispace,
    title={AISPACE at SemEval-2024 task 8: A Class-balanced Soft-voting System for
  Detecting Multi-generator Machine-generated Text},
    author={Renhua Gu and Xiangfeng Meng},
    journal={arXiv preprint arXiv:2404.00950},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.00950},
    primaryClass={cs.CL}
}