Do Large Language Models Plan Answer Positions? Position Bias in Multiple-Choice Question Generation
Large language models (LLMs) are increasingly used to generate multiple-choice questions (MCQs), where correct answers should ideally be uniformly distributed across options. However, we observe that LLMs exhibit systematic position biases during generation. Through extensive experiments with 10 LLMs and 5 vision-language models (VLMs) on three MCQ generation tasks, we show that these biases are structured, with similar patterns emerging within model families. To investigate the underlying mechanisms, we conduct probing experiments and find that hidden representations in the question stem encode predictive signals of the correct answer position, suggesting that answer position may be impl...
| Authors | Xuemei Tang, Xufeng Duan, Zhenguang G. Cai |
|---|---|
| Primary category | cs.CL |
| Published | 3 May 2026 |
| Last updated at source | 3 May 2026 |
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