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arXiv · 2309.02731

HC3 Plus: A Semantic-Invariant Human ChatGPT Comparison Corpus

Zhenpeng Su·Xing Wu·Wei Zhou·Guangyuan Ma·Songlin Hu
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Abstract

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.

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Zhenpeng Su, Xing Wu, Wei Zhou, Guangyuan Ma, Songlin Hu. 2023-09-06. HC3 Plus: A Semantic-Invariant Human ChatGPT Comparison Corpus. https://arxiv.org/abs/2309.02731

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