Large Language Models Systematically Favor Popular Options: Evidence and Mitigation Across MCQs
Computation and Language
Summary
The authors found that large language models (LLMs) tend to pick popular but wrong answers in multiple-choice questions, a problem they call popularity bias. They created PopMCQ, a test that changes how popular the answer choices are while keeping the correct one the same, to study this. To fix the bias, they introduced PopDebias, a simple method that adjusts model predictions without needing extra training. Their tests showed PopDebias improves accuracy on many models, especially when wrong answers are more popular.
large language modelsmultiple-choice questionspopularity biasmodel confidencePopMCQ benchmarkPopDebiasinference-time correctionmodel calibrationdistractor optionsaccuracy
Authors
Abdelrahman Abdallah, Mohammed Ali, Bhawna Piryani, Mahmoud Abdalla, Adam Jatowt
Abstract
Multiple-choice questions (MCQs) are a standard format for evaluating large language models (LLMs), yet the popularity of answer options can confound evaluation. Modern LLMs systematically prefer popular but incorrect options over less popular correct ones, a vulnerability we call \textbf{popularity bias}. This pattern aligns with confidence miscalibration: model confidence remains high even as accuracy collapses for popular options. To systematically isolate this phenomenon, we introduce \textbf{PopMCQ}, a benchmark with six controlled strategies that vary option popularity while keeping the correct answer fixed. In our most adversarial setting, where all distractors are more popular than the correct option, models choose popular but wrong answers 66\% of the time. To mitigate this bias, we propose \textbf{PopDebias}, a lightweight inference-time correction that estimates and removes a popularity prior from model predictions. It requires no fine-tuning, is label-free at test time (using only a small calibration split for parameter fitting), and adds negligible computational cost. Experiments on 22 open-source LLMs (0.5B to 32B parameters) show consistent improvements, with accuracy gains up to 54.1 percentage points under strong popularity pressure. The code and data are available https://github.com/DataScienceUIBK/PopMCQ