Check The Scoreboard: An Analysis of Scoring Schemes on Multiple-Choice Evaluation

Abstract:Multiple-choice question answering (MCQA) benchmarks in NLP use number-right scoring (accuracy), but in educational testing, the scoring scheme, the combination of the response mode models follow and the rule for grading responses, is a key design choice that dictates which abilities to reward. We examine how alternatives to number right change what MCQA measures with six education-inspired schemes that assess abilities beyond accuracy: distractor elimination, abstention, confidence calibration, and self-correction. On LLM benchmarks, these schemes: 1) shift rankings of 31 LLMs beyond rephrased number right prompts; 2) better predict the LLMs users prefer in LLM Arena; and 3) reveal distinct model capabilities, like that GPT-5 rarely abstains and readily self-corrects, while weaker open-weight models often abstain and hesitate to eliminate choices. Given the benefits of alternative scoring schemes, we discuss ways to extend them to tasks beyond MCQA.

Computation and Language (cs.CL)

arXiv:2608.29887 [cs.CL]

(or arXiv:2608.29887v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2608.29887 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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