Beyond Fine-Tuning: In-Context Learning and Chain-of-Thought for Reasoned Distractor Generation
Distractor generation (DG) remains a labor-intensive task that still significantly depends on domain experts. The task focuses on generating plausible yet incorrect options, known as distractors, for multiple-choice questions. A reliable distractor must be contextually relevant to the question and able to mislead examinees through implicit reasoning when identifying the correct answer. While a recent method integrates fine-tuning pre-trained encoder-decoder models with contrastive learning to generate semantically relevant distractors for a given question-answer, it often fails to capture the underlying reasoning process that experts utilize when selecting distractors in benchmarks. In th...
| Authors | Elaf Alhazmi, Quan Z. Sheng, Wei Emma Zhang |
|---|---|
| Primary category | cs.CL |
| Published | 19 April 2026 |
| Last updated at source | 19 April 2026 |
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