arXiv:2602.07267v2 Announce Type: replace-cross Abstract: Evaluating the real-world capabilities of AI systems requires grounding benchmark performance in human-interpretable measures of task difficulty. Existing approaches that rely on direct human task completion time annotations are costly, noisy, and difficult to scale across benchmarks. In this work, we propose BRIDGE, a unified psychometric framework that learns a latent difficulty scale from model responses and anchors it to human task completion time. Using a two-parameter logistic Item Response Theory model, we jointly estimate latent
The explosion of AI models requires increasingly sophisticated and scalable methods for evaluation, moving beyond costly and noisy human annotations towards more efficient, data-driven approaches.
This development offers a principled and scalable way to evaluate AI performance by grounding it in human-interpretable measures, crucial for deploying AI in sensitive applications and accelerating AI development cycles.
Model evaluation becomes more objective and less reliant on expensive, subjective human input, potentially standardizing how AI capabilities are understood and compared.
- · AI developers
- · AI evaluators
- · MLOps platforms
- · Research institutions
- · Manual human annotation services
- · Subjective AI evaluation methods
More accurate and scalable methods for AI system evaluation will emerge, reducing the cost and time of benchmarking.
This framework could accelerate the development and deployment of more reliable AI, linking model performance directly to real-world task difficulty.
Standardized, human-centric evaluation metrics could foster greater public trust in AI systems and influence regulatory approaches to AI safety and performance.
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