LLM Evaluation on Unseen Questions: Contextual Multidimensional IRT Model
Quick summary
arXiv:2608.22295v1 Announce Type: cross Abstract: 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 perform
Key takeaways
- arXiv:2608.22295v1 Announce Type: cross Abstract: 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.
Why it matters
“LLM Evaluation on Unseen Questions: Contextual Multidimensional IRT Model” should be evaluated beyond branding and benchmark scores. Its practical importance will emerge in task accuracy, latency, unit cost, safety and integration with real workflows.
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