arXiv (All)
open access
AI Evaluation Should Require Standardized Item-Level Data Releases
This position paper argues that standardized item-level benchmark data should become the default infrastructure for AI evaluation. Current evaluations suffer from underspecified item selection, construct misalignment, and poor generalization; a disproportionate focus on aggregate model scores compou
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- OSCAR: Order-aware Scoring and Calibration for AI Rankings#1050313
- Evaluating Model Capabilities After Benchmark Saturation#1355656
- What Current AI Benchmarks Leave Unmeasured: Modality, Search, Citations, and Implications (for Safety Evaluations)#1297221
- Your Benchmark Is Not Saturated: Reviving Multiple-Choice Evaluation with Answer Pooling#1141816
- Checkable Is Not Correct: Validity for Verifier Scores in AI Evaluation#113619
Record · ID 1399827
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive