Benchmarking detectors for LLM-generated text typically depends on aggregate metrics like accuracy, F1 score, and AUC. According to a new arXiv paper, these metrics are useful for summarizing overall performance but may leave important measurement questions unanswered.
The paper, titled "A Measurement Perspective on Benchmarking LLM-Generated Text Detectors using Item Response Theory," proposes applying IRT—a psychometric framework used in educational testing—to this problem. IRT models how well a detector performs on individual items (texts) rather than just averaging across a dataset.
The authors argue that this perspective could offer a more granular view of detector strengths and weaknesses, potentially showing how performance varies with text difficulty or other item characteristics. The abstract stops short of presenting results, but the framing suggests a shift toward more principled benchmarking in this rapidly evolving area.