ResearchPod Summary
Modern LLM evaluation is split between two paradigms: static benchmarks, which provide objective correctness labels, and arena-style evaluations, which capture open-ended user preferences. These approaches are often disconnected, treating all evaluation items as equally informative and failing to account for the interaction between model ability and item difficulty. DualEval addresses this by introducing a joint calibration framework that maps both models and evaluation items onto a shared latent scale, inspired by Item Response Theory (IRT).
DualEval assigns each model an ability parameter and each evaluation item both a difficulty and a sharpness parameter. For static benchmarks, it uses a two-parameter logistic IRT model to predict binary correctness. For arena-style data, it converts scalar reward-model scores into soft pairwise preference targets, which are then integrated into the same latent structure. This joint formulation allows the framework to leverage the precision of static benchmarks alongside the realistic, open-ended nature of arena prompts. The model is optimized to balance these signals, effectively using each to compensate for the limitations of the other.
Empirical results across coding, math, domain-knowledge, and generic query domains show that DualEval produces more balanced and stable model rankings than single-source baselines. By learning item-level profiles, the framework enables two key diagnostic applications:
As LLM evaluation becomes increasingly costly and prone to contamination, DualEval provides a principled way to make evaluation pipelines more efficient and interpretable. By treating evaluation data as an object of analysis rather than just a source of aggregate scores, it helps researchers identify which questions actually drive model differentiation and which are redundant or compromised.
AI-generated third-party summary by ResearchPod. Not official content or an endorsement by the paper authors or affiliated organizations.