The advent of Large Language Models (LLMs) has brought an unprecedented surge in machine-generated text (MGT) across diverse channels. This raises legitimate concerns about its potential misuse and societal implications. The need to identify and differentiate such content from genuine human-generated text is critical in combating disinformation, preserving the integrity of education and scientific fields, and maintaining trust in communication. In this work, we address this problem by introducing a new benchmark involving multilingual, multi-domain and multi-generator for MGT detection -- M4GT-Bench. It is collected for three task formulations: (1) mono-lingual and multi-lingual binary MGT detection; (2) multi-way detection identifies which particular model generates the text; and (3) human-machine mixed text detection, where a word boundary delimiting MGT from human-written content should be determined. Human evaluation for Task 2 shows less than random guess performance, demonstrating the challenges to distinguish unique LLMs. Promising results always occur when training and test data distribute within the same domain or generators.

通过引入多语言、多领域和多发生器的机器生成文本检测基准 M4GT-Bench，本研究解决了识别和区分机器生成文本与人类生成文本的问题，包括单语和多语二元机器生成文本检测、多类别检测以确定生成文本具体使用的模型以及在人机混合文本中确定生成文本与人类编写内容之间的边界的挑战。

M4GT-Bench: 黑盒机器生成文本检测评估基准