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            <title>Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)</title>
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    <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.</abstract>
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%0 Conference Proceedings
%T Fine-tuning Language Models for AI vs Human Generated Text detection
%A Bahad, Sankalp
%A Bhaskar, Yash
%A Krishnamurthy, Parameswari
%Y Ojha, Atul Kr.
%Y Doğruöz, A. Seza
%Y Tayyar Madabushi, Harish
%Y Da San Martino, Giovanni
%Y Rosenthal, Sara
%Y Rosá, Aiala
%S Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F bahad-etal-2024-fine-tuning
%X 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.
%R 10.18653/v1/2024.semeval-1.132
%U https://aclanthology.org/2024.semeval-1.132
%U https://doi.org/10.18653/v1/2024.semeval-1.132
%P 918-921

Markdown (Informal)

[Fine-tuning Language Models for AI vs Human Generated Text detection](https://aclanthology.org/2024.semeval-1.132) (Bahad et al., SemEval 2024)

• Fine-tuning Language Models for AI vs Human Generated Text detection (Bahad et al., SemEval 2024)

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.