ArticleProceedings of the conference. Association for Computational Linguistics. North American Chapter. Meeting2025
MCQG-SRefine: Multiple Choice Question Generation and Evaluation with Iterative Self-Critique, Correction, and Comparison Feedback.
Article in Proceedings of the conference. Association for Computational Linguistics. North American Chapter. Meeting, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The trial behind it
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Who cites it
7 citing papers in PubMed.
- A survey on LLM-as-a-judge.Innovation (Cambridge (Mass.)) · 2026Article
- Advancing generative large language models toward discriminative performance in protein function prediction.Genome biology · 2026Article
- DischargeSim: A Simulation Benchmark for Educational Doctor-Patient Communication at Discharge.Proceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing · 2025Article
- From Scores to Steps: Diagnosing and Improving LLM Performance in Evidence-Based Medical Calculations.Proceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing · 2025Article
- Chatbot To Help Patients Understand Their Health.Proceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing · 2025Article
- RARE: Retrieval-Augmented Reasoning Enhancement for Large Language Models.Proceedings of the conference. Association for Computational Linguistics. Meeting · 2025Article
- Prompt engineering for single-best-answer multiple-choice questions in licensing examinations: a narrative review with a case study involving the Korean Medical Licensing Examination.Journal of educational evaluation for health professions · 2025Review
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Automatic question generation (QG) is essential for AI and NLP, particularly in intelligent tutoring, dialogue systems, and fact verification. Generating multiple-choice questions (MCQG) for professional exams, like the United States Medical Licensing Examination (USMLE), is particularly challenging, requiring domain expertise and complex multi-hop reasoning for high-quality questions. However, current large language models (LLMs) like GPT-4 struggle with professional MCQG due to outdated knowledge, hallucination issues, and prompt sensitivity, resulting in unsatisfactory quality and difficulty. To address these challenges, we propose MCQG-SRefine, an LLM self-refine-based (Critique and Correction) framework for converting medical cases into high-quality USMLE-style questions. By integrating expert-driven prompt engineering with iterative self-critique and self-correction feedback, MCQG-SRefine significantly enhances human expert satisfaction regarding both the quality and difficulty of the questions. Furthermore, we introduce an LLM-as-Judge-based automatic metric to replace the complex and costly expert evaluation process, ensuring reliable and expert-aligned assessments.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.