Check The Scoreboard: An Analysis of Scoring Schemes on Multiple-Choice Evaluation
Nishant Balepur, Paiheng Xu, Wei Ai, Eunsol Choi, Rachel Rudinger, Jordan Boyd-Graber · arXiv (2026) · doi:10.48550/arxiv.2608.29887 · gemini-3.1-flash-lite
TL;DR
Current AI tests usually just count how many multiple-choice questions a model gets right, which often encourages guessing. By using alternative scoring methods from traditional education, this study shows that we can better measure a model's true reasoning, its ability to fix mistakes, and how much it actually understands. These new metrics better predict which AI models people will prefer to use in real-world settings.
Method and sample: The study evaluates 31 frontier LLMs (including models from Gemini, GPT, Claude, Llama, and Qwen families) across 1,000 MCQ examples sampled from ARC (grade-school science), MMLU (college knowledge), and SuperGPQA (graduate-level expertise) benchmarks.
#psychometrics#assessment_design#evaluation_metrics#LLM_reasoning#logical_consistency