DeepPavlov at SemEval-2024 Task 8: Leveraging Transfer Learning for Detecting Boundaries of Machine-Generated Texts
This addresses the nuanced detection of machine-generated text boundaries for applications in collaborative human-AI writing, though it appears incremental as it builds on existing transfer learning methods.
The paper tackled the problem of detecting boundaries between human-written and machine-generated texts in hybrid human-AI writing, presenting a pipeline for data augmentation and fine-tuning DeBERTaV3 that achieved a new best MAE score on the SemEval-2024 competition leaderboard.
The Multigenerator, Multidomain, and Multilingual Black-Box Machine-Generated Text Detection shared task in the SemEval-2024 competition aims to tackle the problem of misusing collaborative human-AI writing. Although there are a lot of existing detectors of AI content, they are often designed to give a binary answer and thus may not be suitable for more nuanced problem of finding the boundaries between human-written and machine-generated texts, while hybrid human-AI writing becomes more and more popular. In this paper, we address the boundary detection problem. Particularly, we present a pipeline for augmenting data for supervised fine-tuning of DeBERTaV3. We receive new best MAE score, according to the leaderboard of the competition, with this pipeline.