IMGTB: A Framework for Machine-Generated Text Detection Benchmarking
Michal Spiegel Dominik Macko
In the era of large language models generating high quality texts, it is a
necessity to develop methods for detection of machine-generated text to avoid
harmful use or simply due to annotation purposes. It is, however, also
important to properly evaluate and compare such developed methods. Recently, a
few benchmarks have been proposed for this purpose; however, integration of
newest detection methods is rather challenging, since new methods appear each
month and provide slightly different evaluation pipelines. In this paper, we
present the IMGTB framework, which simplifies the benchmarking of
machine-generated text detection methods by easy integration of custom (new)
methods and evaluation datasets. Its configurability and flexibility makes
research and development of new detection methods easier, especially their
comparison to the existing state-of-the-art detectors. The default set of
analyses, metrics and visualizations offered by the tool follows the
established practices of machine-generated text detection benchmarking found in
state-of-the-art literature.