Summary of Mcqg-srefine: Multiple Choice Question Generation and Evaluation with Iterative Self-critique, Correction, and Comparison Feedback, by Zonghai Yao et al.
MCQG-SRefine: Multiple Choice Question Generation and Evaluation with Iterative Self-Critique, Correction, and Comparison Feedback
by Zonghai Yao, Aditya Parashar, Huixue Zhou, Won Seok Jang, Feiyun Ouyang, Zhichao Yang, Hong Yu
First submitted to arxiv on: 17 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a new framework called MCQG-SRefine for generating high-quality multiple-choice questions (MCQs) for professional exams like the United States Medical Licensing Examination (USMLE). The framework uses large language models (LLMs) to convert medical cases into USMLE-style questions, and integrates expert-driven prompt engineering with iterative self-critique and self-correction feedback. This approach addresses challenges in current LLM-based MCQG systems, such as outdated knowledge, hallucination issues, and prompt sensitivity. The proposed framework significantly enhances human expert satisfaction regarding both the quality and difficulty of the generated questions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about creating a new way to make questions for professional exams. Right now, computers struggle to make good questions that are hard enough but not too hard. To fix this, researchers created a new system called MCQG-SRefine. It uses special computer models and expert feedback to make better questions. This new system makes questions that experts like the ones who take the exam think are really good. |
Keywords
» Artificial intelligence » Hallucination » Prompt
