Abstract
In this paper, we introduce a machine-generated text detection system designed totackle the challenges posed by the prolifera-tion of large language models (LLMs). Withthe rise of LLMs such as ChatGPT and GPT-4,there is a growing concern regarding the po-tential misuse of machine-generated content,including misinformation dissemination. Oursystem addresses this issue by automating theidentification of machine-generated text acrossmultiple subtasks: binary human-written vs.machine-generated text classification, multi-way machine-generated text classification, andhuman-machine mixed text detection. We em-ploy the RoBERTa Base model and fine-tuneit on a diverse dataset encompassing variousdomains, languages, and sources. Throughrigorous evaluation, we demonstrate the effec-tiveness of our system in accurately detectingmachine-generated text, contributing to effortsaimed at mitigating its potential misuse.- Anthology ID:
- 2024.semeval-1.132
- 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:
- 918–921
- Language:
- URL:
- https://aclanthology.org/2024.semeval-1.132
- DOI:
- Cite (ACL):
- Sankalp Bahad, Yash Bhaskar, and Parameswari Krishnamurthy. 2024. Fine-tuning Language Models for AI vs Human Generated Text detection. In Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024), pages 918–921, Mexico City, Mexico. Association for Computational Linguistics.
- Cite (Informal):
- Fine-tuning Language Models for AI vs Human Generated Text detection (Bahad et al., SemEval 2024)
- PDF:
- https://preview.aclanthology.org/jeptaln-2024-ingestion/2024.semeval-1.132.pdf