Team MLab at SemEval-2024 Task 8: Analyzing Encoder Embeddings for Detecting LLM-generated Text
Kevin Li, Kenan Hasanaliyev, Sally Zhu, George Altshuler, Alden Eberts, Eric Chen, Kate Wang, Emily Xia, Eli Browne, Ian Chen
Abstract
This paper explores solutions to the challenges posed by the widespread use of LLMs, particularly in the context of identifying human-written versus machine-generated text. Focusing on Subtask B of SemEval 2024 Task 8, we compare the performance of RoBERTa and DeBERTa models. Subtask B involved identifying not only human or machine text but also the specific LLM responsible for generating text, where our DeBERTa model outperformed the RoBERTa baseline by over 10% in leaderboard accuracy. The results highlight the rapidly growing capabilities of LLMs and importance of keeping up with the latest advancements. Additionally, our paper presents visualizations using PCA and t-SNE that showcase the DeBERTa model’s ability to cluster different LLM outputs effectively. These findings contribute to understanding and improving AI methods for detecting machine-generated text, allowing us to build more robust and traceable AI systems in the language ecosystem.- Anthology ID:
- 2024.semeval-1.210
- Volume:
- Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)
- Month:
- June
- Year:
- 2024
- Address:
- Mexico City, Mexico
- Editors:
- Atul Kr. Ojha, A. Seza Doğruöz, Harish Tayyar Madabushi, Giovanni Da San Martino, Sara Rosenthal, Aiala Rosá
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1463–1467
- Language:
- URL:
- https://aclanthology.org/2024.semeval-1.210
- DOI:
- Cite (ACL):
- Kevin Li, Kenan Hasanaliyev, Sally Zhu, George Altshuler, Alden Eberts, Eric Chen, Kate Wang, Emily Xia, Eli Browne, and Ian Chen. 2024. Team MLab at SemEval-2024 Task 8: Analyzing Encoder Embeddings for Detecting LLM-generated Text. In Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024), pages 1463–1467, Mexico City, Mexico. Association for Computational Linguistics.
- Cite (Informal):
- Team MLab at SemEval-2024 Task 8: Analyzing Encoder Embeddings for Detecting LLM-generated Text (Li et al., SemEval 2024)
- PDF:
- https://preview.aclanthology.org/jeptaln-2024-ingestion/2024.semeval-1.210.pdf