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

• URL: http://arxiv.org/abs/2608.29887v1
• Date: Sun, 30 Aug 2026 16:35:22 GMT
• Title: Check The Scoreboard: An Analysis of Scoring Schemes on Multiple-Choice Evaluation
• Abstract summary: Multiple-choice question answering (MCQA) benchmarks in NLP use number-right scoring (accuracy)<n>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.<n>We examine how alternatives to number right change what MCQA measures with six education-inspired schemes that assess abilities beyond accuracy.
• Score: 52.58737865652009
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Boosting Self-Consistency with Ranking [56.38798757709555]
  Self-consistency improves large language models by sampling multiple reasoning paths and selecting the most frequent answer.<n>We address this limitation with Ranking-Improved Self-Consistency (RISC), which reformulates answer selection in self-consistency as a ranking problem.
  arXiv  Detail & Related papers  (2026-06-03T16:12:30Z)
• ABCD: All Biases Come Disguised [4.603755953026689]
  Multiple-choice question (MCQ) benchmarks have been a standard evaluation practice.<n>We propose a simple bias-reduced evaluation protocol that replaces the labels of each question with uniform, unordered labels.<n>We show that this protocol substantially improves the robustness to answer permutations, reducing mean accuracy variance $3times$ with only a minimal decrease in the mean model's performance.
  arXiv  Detail & Related papers  (2026-02-19T15:12:33Z)
• Scoring, Reasoning, and Selecting the Best! Ensembling Large Language Models via a Peer-Review Process [58.265053900416895]
  LLM-PeerReview is built on a novel, peer-review-inspired framework.<n>It operates in three stages: For scoring, we use the emerging LLM-as-a-Judge technique.<n>For reasoning, we can apply a graphical model-based truth inference algorithm.<n>Finally, the highest-scoring response is selected as the best ensemble output.
  arXiv  Detail & Related papers  (2025-12-29T05:25:49Z)
• Quantifying and Mitigating Selection Bias in LLMs: A Transferable LoRA Fine-Tuning and Efficient Majority Voting Approach [13.829059542429876]
  Multiple Choice Question (MCQ) answering is a widely used method for evaluating the performance of Large Language Models (LLMs)<n>LLMs often exhibit selection bias in MCQ tasks, where their choices are influenced by factors like answer position or option symbols rather than the content.
  arXiv  Detail & Related papers  (2025-11-17T21:31:37Z)
• Right Answer, Wrong Score: Uncovering the Inconsistencies of LLM Evaluation in Multiple-Choice Question Answering [78.89231943329885]
  Multiple-Choice Question Answering (MCQA) is widely used to evaluate Large Language Models (LLMs)<n>We show that multiple factors can significantly impact the reported performance of LLMs.<n>We analyze whether existing answer extraction methods are aligned with human judgment.
  arXiv  Detail & Related papers  (2025-03-19T08:45:03Z)
• Open-LLM-Leaderboard: From Multi-choice to Open-style Questions for LLMs Evaluation, Benchmark, and Arena [23.264049073539663]
  Multiple-choice questions (MCQ) are frequently used to assess large language models (LLMs) LLMs may inherently favor certain answer choice IDs, such as A/B/C/D, due to inherent biases of priori unbalanced probabilities. This work aims to tackle these significant difficulties, and establish a new LLM evaluation benchmark through entirely open-style questions.
  arXiv  Detail & Related papers  (2024-06-11T17:59:47Z)
• Artifacts or Abduction: How Do LLMs Answer Multiple-Choice Questions Without the Question? [15.308093827770474]
  We probe if large language models (LLMs) can perform multiple-choice question answering (MCQA) with choices-only prompts. This prompt bests a majority baseline in 11/12 cases, with up to 0.33 accuracy gain. We conduct an in-depth, black-box analysis on memorization, choice dynamics, and question inference.
  arXiv  Detail & Related papers  (2024-02-19T19:38:58Z)
• Self-Evaluation Improves Selective Generation in Large Language Models [54.003992911447696]
  We reformulate open-ended generation tasks into token-level prediction tasks. We instruct an LLM to self-evaluate its answers. We benchmark a range of scoring methods based on self-evaluation.
  arXiv  Detail & Related papers  (2023-12-14T19:09:22Z)
• Large Language Models Are Not Robust Multiple Choice Selectors [117.72712117510953]
  Multiple choice questions (MCQs) serve as a common yet important task format in the evaluation of large language models (LLMs) This work shows that modern LLMs are vulnerable to option position changes due to their inherent "selection bias" We propose a label-free, inference-time debiasing method, called PriDe, which separates the model's prior bias for option IDs from the overall prediction distribution.
  arXiv  Detail & Related papers  (2023-09-07T17:44:56Z)
• SelfCheck: Using LLMs to Zero-Shot Check Their Own Step-by-Step Reasoning [55.76083560152823]
  SelfCheck is a general-purpose zero-shot verification schema for recognizing errors in step-by-step reasoning. We test SelfCheck on three datasets (GSM8K, MathQA, and MATH) and find that it successfully recognizes errors and, in turn, increases final answer accuracies.
  arXiv  Detail & Related papers  (2023-08-01T10:31:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.