LLM Evaluation on Unseen Questions: Contextual Multidimensional IRT Model
Evaluation of large language models (LLMs) increasingly requires predicting how a model will perform on new questions or tasks before collecting large amounts of new annotations. This problem is challenging because question difficulty, scenario, and underlying capability demands can vary substantially. Simple retrospective averages may confound model ability with item characteristics. In this paper, we study a model-based evaluation framework that combines multidimensional item response theory model with question contexts to predict LLM performance on unseen questions. The framework represents LLMs through latent capability profiles while using question content to inform item characterist...
| Authors | Ergan Shang, Weijing Tang, Yinqiu He |
|---|---|
| Primary category | cs.CL |
| Published | 23 August 2026 |
| Last updated at source | 23 August 2026 |
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