Standardized Item-Level Data Releases Urged for AI Evaluation
A recent position paper advocates for the establishment of standardized item-level benchmark data as the foundational framework for evaluating AI. Present assessments are hindered by poorly defined item selection, misalignment with constructs, and inadequate generalization, primarily due to an overemphasis on aggregate model scores. Without item-level data, it becomes impossible to validate claims, resulting in exaggerated assertions of capabilities, misguided research efforts, and misplaced confidence in operational systems. The authors argue that valid evaluations must rely on empirical evidence from item-level responses, suggesting that the standardized release of such data is essential for enhancing transparency, replicability, and auditability in AI evaluation. To illustrate its practicality, they created OpenEval, an item-level repository containing 10 million responses across 155,000 items.
Key facts
- Paper argues for standardized item-level benchmark data as default AI evaluation infrastructure.
- Current evaluations suffer from underspecified item selection, construct misalignment, and poor generalization.
- Root cause is misplaced focus on aggregate model scores.
- Without item-level evidence, validity claims cannot be assessed.
- Standardized release enables transparency, replicability, and auditability.
- Authors constructed OpenEval, an item-level archive of 10M responses across 155k items.
- Paper is a position paper from arXiv (2604.03244v2).
- Focus is on improving AI evaluation validity.
Entities
Institutions
- arXiv