M4GT-Bench: Evaluation Benchmark for Black-Box Machine-Generated Text Detection
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.
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