Leveraging Computerized Adaptive Testing for Cost-effective Evaluation of Large Language Models in Medical Benchmarking
arXiv:2603.23506v1 Announce Type: new Abstract: The rapid proliferation of large language models (LLMs) in healthcare creates an urgent need for scalable and psychometrically sound evaluation methods. Conventional static benchmarks are costly to administer repeatedly, vulnerable to data contamination, and lack calibrated measurement properties for fine-grained performance tracking. We propose and validate a computerized adaptive testing (CAT) framework grounded in item response theory (IRT) for efficient assessment of standardized medical knowledge in LLMs. The study comprises a two-phase design: a Monte Carlo simulation to identify optimal CAT configurations and an empirical evaluation of 38 LLMs using a human-calibrated medical item bank. Each model completed both the full item bank and an adaptive test that dynamically selected items based on real-time ability estimates and terminated upon reaching a predefined reliability threshold (standard error <= 0.3). Results show that CAT-de
arXiv:2603.23506v1 Announce Type: new Abstract: The rapid proliferation of large language models (LLMs) in healthcare creates an urgent need for scalable and psychometrically sound evaluation methods. Conventional static benchmarks are costly to administer repeatedly, vulnerable to data contamination, and lack calibrated measurement properties for fine-grained performance tracking. We propose and validate a computerized adaptive testing (CAT) framework grounded in item response theory (IRT) for efficient assessment of standardized medical knowledge in LLMs. The study comprises a two-phase design: a Monte Carlo simulation to identify optimal CAT configurations and an empirical evaluation of 38 LLMs using a human-calibrated medical item bank. Each model completed both the full item bank and an adaptive test that dynamically selected items based on real-time ability estimates and terminated upon reaching a predefined reliability threshold (standard error <= 0.3). Results show that CAT-derived proficiency estimates achieved a near-perfect correlation with full-bank estimates (r = 0.988) while using only 1.3 percent of the items. Evaluation time was reduced from several hours to minutes per model, with substantial reductions in token usage and computational cost, while preserving inter-model performance rankings. This work establishes a psychometric framework for rapid, low-cost benchmarking of foundational medical knowledge in LLMs. The proposed adaptive methodology is intended as a standardized pre-screening and continuous monitoring tool and is not a substitute for real-world clinical validation or safety-oriented prospective studies.
Executive Summary
This article proposes a computerized adaptive testing (CAT) framework to evaluate large language models (LLMs) in a cost-effective and scalable manner. The authors validate their approach through a two-phase design, demonstrating that CAT-derived proficiency estimates correlate highly with full-bank estimates while reducing evaluation time by several orders of magnitude. This work establishes a psychometric framework for rapid and low-cost benchmarking of foundational medical knowledge in LLMs, addressing a pressing need in the field. The proposed adaptive methodology is intended as a standardized pre-screening and continuous monitoring tool, although it is not a substitute for real-world clinical validation or safety-oriented prospective studies. This innovation has significant implications for the development and deployment of LLMs in healthcare, enabling more efficient evaluation and validation of these models.
Key Points
- ▸ The authors propose a CAT framework grounded in item response theory (IRT) for efficient assessment of standardized medical knowledge in LLMs.
- ▸ The study demonstrates a high correlation (r = 0.988) between CAT-derived proficiency estimates and full-bank estimates.
- ▸ The proposed adaptive methodology reduces evaluation time by several orders of magnitude and preserves inter-model performance rankings.
Merits
Strength
The CAT framework is grounded in psychometric theory (IRT), ensuring a sound and reliable evaluation method.
Cost-effectiveness
The adaptive methodology significantly reduces evaluation time and computational cost while preserving performance rankings.
Scalability
The proposed framework enables efficient evaluation of large numbers of LLMs, addressing a pressing need in the field.
Demerits
Limitation
The study does not address the clinical validity or safety of LLMs in real-world settings, highlighting the need for complementary validation studies.
Assumptions
The authors assume a representative human-calibrated medical item bank, which may not generalize to all LLMs or medical domains.
Complexity
The CAT framework may require significant computational resources and expertise to implement and maintain.
Expert Commentary
This article makes a significant contribution to the field of LLM evaluation and validation, proposing a cost-effective and scalable framework that addresses a pressing need in healthcare. The authors demonstrate the effectiveness of their approach through a rigorous two-phase design, showcasing a high correlation between CAT-derived proficiency estimates and full-bank estimates. While the study has limitations, it highlights the importance of standardized evaluation methods for LLMs and emphasizes the need for complementary validation studies. As LLMs continue to proliferate in healthcare, this innovation has significant implications for the development and deployment of these models, enabling more efficient evaluation and validation while preserving performance rankings.
Recommendations
- ✓ Future studies should focus on adapting the CAT framework to address concerns about fairness and bias in LLMs.
- ✓ Regulatory frameworks and guidelines should be developed to standardize evaluation methods for LLMs and ensure their safe and effective deployment in healthcare.
Sources
Original: arXiv - cs.CL
