  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 based on a multilingual,
multi-domain, and multi-generator corpus of MGTs -- M4GT-Bench. The benchmark
is compiled of three tasks: (1) mono-lingual and multi-lingual binary MGT
detection; (2) multi-way detection where one need to identify, which particular
model generated the text; and (3) mixed human-machine text detection, where a
word boundary delimiting MGT from human-written content should be determined.
On the developed benchmark, we have tested several MGT detection baselines and
also conducted an evaluation of human performance. We see that obtaining good
performance in MGT detection usually requires an access to the training data
from the same domain and generators. The benchmark is available at
https://github.com/mbzuai-nlp/M4GT-Bench.
