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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2309.02731 |
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| _version_ | 1866914967288020992 |
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| author | Su, Zhenpeng Wu, Xing Zhou, Wei Ma, Guangyuan Hu, Songlin |
| author_facet | Su, Zhenpeng Wu, Xing Zhou, Wei Ma, Guangyuan Hu, Songlin |
| contents | ChatGPT has garnered significant interest due to its impressive performance; however, there is growing concern about its potential risks, particularly in the detection of AI-generated content (AIGC), which is often challenging for untrained individuals to identify. Current datasets used for detecting ChatGPT-generated text primarily focus on question-answering tasks, often overlooking tasks with semantic-invariant properties, such as summarization, translation, and paraphrasing. In this paper, we demonstrate that detecting model-generated text in semantic-invariant tasks is more challenging. To address this gap, we introduce a more extensive and comprehensive dataset that incorporates a wider range of tasks than previous work, including those with semantic-invariant properties. In addition, instruction fine-tuning has demonstrated superior performance across various tasks. In this paper, we explore the use of instruction fine-tuning models for detecting text generated by ChatGPT. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_02731 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | HC3 Plus: A Semantic-Invariant Human ChatGPT Comparison Corpus Su, Zhenpeng Wu, Xing Zhou, Wei Ma, Guangyuan Hu, Songlin Computation and Language Artificial Intelligence ChatGPT has garnered significant interest due to its impressive performance; however, there is growing concern about its potential risks, particularly in the detection of AI-generated content (AIGC), which is often challenging for untrained individuals to identify. Current datasets used for detecting ChatGPT-generated text primarily focus on question-answering tasks, often overlooking tasks with semantic-invariant properties, such as summarization, translation, and paraphrasing. In this paper, we demonstrate that detecting model-generated text in semantic-invariant tasks is more challenging. To address this gap, we introduce a more extensive and comprehensive dataset that incorporates a wider range of tasks than previous work, including those with semantic-invariant properties. In addition, instruction fine-tuning has demonstrated superior performance across various tasks. In this paper, we explore the use of instruction fine-tuning models for detecting text generated by ChatGPT. |
| title | HC3 Plus: A Semantic-Invariant Human ChatGPT Comparison Corpus |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2309.02731 |