A Measurement Perspective on Benchmarking LLM-Generated Text Detectors using Item Response Theory
Benchmarking large language model (LLM)-generated text detectors commonly relies on aggregate detector-level metrics such as accuracy, F1 score, and AUC. Although useful for summarizing overall performance, these metrics provide limited insight into how benchmark composition shapes detector comparisons or which texts distinguish detectors with different response profiles. We propose a measurement-based benchmarking framework for jointly characterizing benchmark heterogeneity and detector behavior using multidimensional item response theory (IRT). By treating benchmark texts as measurement items and detectors as respondents, the framework estimates text-level discrimination and difficulty-...
| Authors | Yanbin Zhao, Zhipeng Chen, Guangsheng Bao et al. |
|---|---|
| Primary category | cs.CL |
| Published | 18 October 2026 |
| Last updated at source | 18 October 2026 |
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