Abstract
Large language models (LLMs) such as ChatGPT have exhibited remarkable performance in generating human-like texts. However, machine-generated texts (MGTs) may carry critical risks, such as plagiarism issues and hallucination information. Therefore, it is very urgent and important to detect MGTs in many situations. Unfortunately, it is challenging to distinguish MGTs and human-written texts because the distributional discrepancy between them is often very subtle due to the remarkable performance of LLMS. In this paper, we seek to exploit \textit{maximum mean discrepancy} (MMD) to address this issue in the sense that MMD can well identify distributional discrepancies. However, directly training a detector with MMD using diverse MGTs will incur a significantly increased variance of MMD since MGTs may contain \textit{multiple text populations} due to various LLMs. This will severely impair MMD's ability to measure the difference between two samples. To tackle this, we propose a novel \textit{multi-population} aware optimization method for MMD called MMD-MP, which can \textit{avoid variance increases} and thus improve the stability to measure the distributional discrepancy. Relying on MMD-MP, we develop two methods for paragraph-based and sentence-based detection, respectively. Extensive experiments on various LLMs, \eg, GPT2 and ChatGPT, show superior detection performance of our MMD-MP.
Original language | English |
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Title of host publication | Proceedings of the Twelfth International Conference on Learning Representations, ICLR 2024 |
Publisher | International Conference on Learning Representations |
Pages | 1-36 |
Number of pages | 18 |
Publication status | Published - May 2024 |
Event | 12th International Conference on Learning Representations, ICLR 2024 - Messe Wien Exhibition and Congress Center, Vienna, Austria Duration: 7 May 2024 → 11 May 2024 https://iclr.cc/Conferences/2024 (Conference website) https://iclr.cc/virtual/2024/calendar (Conference schedule ) https://openreview.net/group?id=ICLR.cc/2024/Conference#tab-accept-oral (Conference proceedings) |
Publication series
Name | Proceedings of the International Conference on Learning Representations, ICLR |
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Conference
Conference | 12th International Conference on Learning Representations, ICLR 2024 |
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Country/Territory | Austria |
City | Vienna |
Period | 7/05/24 → 11/05/24 |
Internet address |
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Scopus Subject Areas
- Language and Linguistics
- Computer Science Applications
- Education
- Linguistics and Language