Mast Kalandar at SemEval-2024 Task 8: On the Trail of Textual Origins: RoBERTa-BiLSTM Approach to Detect AI-Generated Text
<|reference_start|>Mast Kalandar at SemEval-2024 Task 8: On the Trail of Textual Origins: RoBERTa-BiLSTM Approach to Detect AI-Generated Text: Large Language Models (LLMs) have showcased impressive abilities in generating fluent responses to diverse user queries. However, concerns regarding the potential misuse of such texts in journalism, educational, and academic contexts have surfaced. SemEval 2024 introduces the task of Multigenerator, Multidomain, and Multilingual Black-Box Machine-Generated Text Detection, aiming to develop automated systems for identifying machine-generated text and detecting potential misuse. In this paper, we i) propose a RoBERTa-BiLSTM based classifier designed to classify text into two categories: AI-generated or human ii) conduct a comparative study of our model with baseline approaches to evaluate its effectiveness. This paper contributes to the advancement of automatic text detection systems in addressing the challenges posed by machine-generated text misuse. Our architecture ranked 46th on the official leaderboard with an accuracy of 80.83 among 125.<|reference_end|>
@article{bafna2024mast,
    title={Mast Kalandar at SemEval-2024 Task 8: On the Trail of Textual Origins:
  RoBERTa-BiLSTM Approach to Detect AI-Generated Text},
    author={Jainit Sushil Bafna, Hardik Mittal, Suyash Sethia, Manish Shrivastava,
  Radhika Mamidi},
    journal={arXiv preprint arXiv:2407.02978},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.02978},
    primaryClass={cs.CL cs.AI}
}