Beyond Fine-Tuning: In-Context Learning and Chain-of-Thought for Reasoned Distractor Generation

• URL: http://arxiv.org/abs/2604.17574v1
• Date: Sun, 19 Apr 2026 18:29:28 GMT
• Title: Beyond Fine-Tuning: In-Context Learning and Chain-of-Thought for Reasoned Distractor Generation
• Abstract summary: Distractor generation (DG) remains a labor-intensive task that still significantly depends on domain experts.<n>In this paper, we explore large language models (LLMs) reasoning for DG through in-context learning with unsupervised semantic retrieval.<n>We design a rationale-augmented DG framework that jointly generates distractors and their rationales for a given question-answer.
• Score: 17.578631264880876
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 this paper, we explore large language models (LLMs) reasoning for DG through in-context learning with unsupervised semantic retrieval for selecting few-shot examples. We design a rationale-augmented DG framework that jointly generates distractors and their rationales for a given question-answer. Extensive experiments on six benchmarks, with varying average distractor lengths and domains, demonstrate that prompting LLMs with few-shot examples substantially improves the performance compared to recent DG models. It outperforms recent approaches and achieves state-of-the-art results in generating reasoned distractors that align with human-labeled benchmarks.

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