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MGTEVAL is introduced as a platform to standardize the evaluation of machine-generated text (MGT) detectors by unifying datasets, attacks, and metrics. The platform allows for the construction of custom benchmarks using configurable LLMs for MGT generation, application of 12 text attacks, and training of detectors through a unified interface. MGTEVAL reports effectiveness, robustness, and efficiency of MGT detectors through both command-line and Web-based interfaces, improving reproducibility and comparability.
Stop wrestling with fragmented MGT detection benchmarks: MGTEVAL offers a unified platform to build, attack, train, and evaluate detectors with ease.
We present MGTEVAL, an extensible platform for systematic evaluation of Machine-Generated Text (MGT) detectors. Despite rapid progress in MGT detection, existing evaluations are often fragmented across datasets, preprocessing, attacks, and metrics, making results hard to compare and reproduce. MGTEVAL organizes the workflow into four components: Dataset Building, Dataset Attack, Detector Training, and Performance Evaluation. It supports constructing custom benchmarks by generating MGT with configurable LLMs, applying 12 text attacks to test sets, training detectors via a unified interface, and reporting effectiveness, robustness, and efficiency. The platform provides both command-line and Web-based interfaces for user-friendly experimentation without code rewriting.
