RAID: A Shared Benchmark for Robust Evaluation of Machine-Generated Text Detectors
This work addresses the need for robust evaluation of text detectors for researchers and practitioners, though it is incremental as it builds on existing benchmarking efforts.
The authors tackled the problem of evaluating machine-generated text detectors by creating RAID, a large and challenging benchmark dataset, and found that current detectors are easily fooled by adversarial attacks and other variations.
Many commercial and open-source models claim to detect machine-generated text with extremely high accuracy (99% or more). However, very few of these detectors are evaluated on shared benchmark datasets and even when they are, the datasets used for evaluation are insufficiently challenging-lacking variations in sampling strategy, adversarial attacks, and open-source generative models. In this work we present RAID: the largest and most challenging benchmark dataset for machine-generated text detection. RAID includes over 6 million generations spanning 11 models, 8 domains, 11 adversarial attacks and 4 decoding strategies. Using RAID, we evaluate the out-of-domain and adversarial robustness of 8 open- and 4 closed-source detectors and find that current detectors are easily fooled by adversarial attacks, variations in sampling strategies, repetition penalties, and unseen generative models. We release our data along with a leaderboard to encourage future research.