Fine-tuning Large Language Models for Multigenerator, Multidomain, and Multilingual Machine-Generated Text Detection
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866911762970836992 |
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| author | Xiong, Feng Markchom, Thanet Zheng, Ziwei Jung, Subin Ojha, Varun Liang, Huizhi |
| author_facet | Xiong, Feng Markchom, Thanet Zheng, Ziwei Jung, Subin Ojha, Varun Liang, Huizhi |
| contents | SemEval-2024 Task 8 introduces the challenge of identifying machine-generated texts from diverse Large Language Models (LLMs) in various languages and domains. The task comprises three subtasks: binary classification in monolingual and multilingual (Subtask A), multi-class classification (Subtask B), and mixed text detection (Subtask C). This paper focuses on Subtask A & B. Each subtask is supported by three datasets for training, development, and testing. To tackle this task, two methods: 1) using traditional machine learning (ML) with natural language preprocessing (NLP) for feature extraction, and 2) fine-tuning LLMs for text classification. The results show that transformer models, particularly LoRA-RoBERTa, exceed traditional ML methods in effectiveness, with majority voting being particularly effective in multilingual contexts for identifying machine-generated texts. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_12326 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Fine-tuning Large Language Models for Multigenerator, Multidomain, and Multilingual Machine-Generated Text Detection Xiong, Feng Markchom, Thanet Zheng, Ziwei Jung, Subin Ojha, Varun Liang, Huizhi Computation and Language Artificial Intelligence SemEval-2024 Task 8 introduces the challenge of identifying machine-generated texts from diverse Large Language Models (LLMs) in various languages and domains. The task comprises three subtasks: binary classification in monolingual and multilingual (Subtask A), multi-class classification (Subtask B), and mixed text detection (Subtask C). This paper focuses on Subtask A & B. Each subtask is supported by three datasets for training, development, and testing. To tackle this task, two methods: 1) using traditional machine learning (ML) with natural language preprocessing (NLP) for feature extraction, and 2) fine-tuning LLMs for text classification. The results show that transformer models, particularly LoRA-RoBERTa, exceed traditional ML methods in effectiveness, with majority voting being particularly effective in multilingual contexts for identifying machine-generated texts. |
| title | Fine-tuning Large Language Models for Multigenerator, Multidomain, and Multilingual Machine-Generated Text Detection |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2401.12326 |