2024
SEMEVAL
SemEval 2024
PetKaz at SemEval-2024 Task 8: Can Linguistics Capture the Specifics of LLM-generated Text?
Abstract
AbstractIn this paper, we present our submission to the SemEval-2024 Task 8 “Multigenerator, Multidomain, and Multilingual Black-Box Machine-Generated Text Detection”, focusing on the detection of machine-generated texts (MGTs) in English. Specifically, our approach relies on combining embeddings from the RoBERTa-base with diversity features and uses a resampled training set. We score 16th from 139 in the ranking for Subtask A, and our results show that our approach is generalizable across unseen models and domains, achieving an accuracy of 0.91.
❓
The Questioner
🌉
Interdisciplinary Bridge
— Computer Science and Deep Learning and Machine Learning and Natural Language Processing
🐝
Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Security & Privacy, Speech & Audio
Authors
Topics
Machine Learning > Core Methods > Classification
Deep Learning > Architectures > Transformers
Natural Language Processing > Applications > Text Classification
Computer Science > Applications > Cybersecurity
Computer Science > Applications > Document Analysis
Machine Learning > Learning Types > Supervised Learning