On the Detectability of ChatGPT Content: Benchmarking, Methodology, and Evaluation through the Lens of Academic Writing
arxiv(2023)
摘要
With ChatGPT under the spotlight, utilizing large language models (LLMs) to
assist academic writing has drawn a significant amount of debate in the
community. In this paper, we aim to present a comprehensive study of the
detectability of ChatGPT-generated content within the academic literature,
particularly focusing on the abstracts of scientific papers, to offer holistic
support for the future development of LLM applications and policies in
academia. Specifically, we first present GPABench2, a benchmarking dataset of
over 2.8 million comparative samples of human-written, GPT-written,
GPT-completed, and GPT-polished abstracts of scientific writing in computer
science, physics, and humanities and social sciences. Second, we explore the
methodology for detecting ChatGPT content. We start by examining the
unsatisfactory performance of existing ChatGPT detecting tools and the
challenges faced by human evaluators (including more than 240 researchers or
students). We then test the hand-crafted linguistic features models as a
baseline and develop a deep neural framework named CheckGPT to better capture
the subtle and deep semantic and linguistic patterns in ChatGPT written
literature. Last, we conduct comprehensive experiments to validate the proposed
CheckGPT framework in each benchmarking task over different disciplines. To
evaluate the detectability of ChatGPT content, we conduct extensive experiments
on the transferability, prompt engineering, and robustness of CheckGPT.
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