Leveraging Computerized Adaptive Testing for Cost-effective Evaluation of Large Language Models in Medical Benchmarking

2026-03-26 · Source: cs.CL updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Advanced, quick

Summary

A new computerized adaptive testing (CAT) framework, based on item response theory (IRT), has been developed to efficiently evaluate large language models (LLMs) in medical benchmarking. This framework addresses the high cost, data contamination risks, and lack of calibrated measurement properties associated with conventional static benchmarks. The study involved a two-phase design: a Monte Carlo simulation to optimize CAT configurations and an empirical evaluation of 38 LLMs using a human-calibrated medical item bank. The CAT method dynamically selected items based on real-time ability estimates, terminating when a reliability threshold (standard error <= 0.3) was met. Results demonstrated that CAT-derived proficiency estimates correlated almost perfectly with full-bank estimates (r = 0.988) while utilizing only 1.3 percent of the items. This reduced evaluation time from hours to minutes per model, significantly cutting token usage and computational costs, all while maintaining inter-model performance rankings.

Key takeaway

For AI scientists and NLP engineers evaluating LLMs in healthcare, adopting a CAT framework can drastically cut evaluation costs and time without sacrificing accuracy. Your team can use this method for rapid pre-screening and continuous monitoring of foundational medical knowledge in LLMs, freeing up resources for more complex real-world clinical validation and safety studies. This approach offers a psychometrically sound, scalable alternative to traditional static benchmarks.

Key insights

Adaptive testing significantly reduces LLM evaluation costs while maintaining high accuracy in medical benchmarking.

Principles

• Item Response Theory enables adaptive testing.
• Dynamic item selection improves evaluation efficiency.

Method

The method uses a two-phase design: Monte Carlo simulation for CAT configuration and empirical evaluation of LLMs with a human-calibrated medical item bank, terminating tests at a predefined reliability threshold.

In practice

• Use CAT for rapid LLM pre-screening.
• Implement CAT for continuous LLM monitoring.

Topics

• Large Language Models
• Medical Benchmarking
• Computerized Adaptive Testing
• Item Response Theory
• Psychometric Evaluation

Best for: Machine Learning Engineer, NLP Engineer, AI Scientist, AI Researcher, AI Engineer, AI Data Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CL updates on arXiv.org.