Papers with question generation framework
DRAGOn: Designing RAG On Periodically Updated Corpus (2026.eacl-srw)
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Fedor Chernogorskii, Sergei Averkiev, Liliya Kudraleeva, Zaven Martirosian, Maria Tikhonova, Valentin Malykh, Alena Fenogenova
| Challenge: | Existing methods for evaluating RAG systems are labor-intensive and difficult to maintain. |
| Approach: | They propose a method to design a RAG benchmark on a regularly updated corpus. |
| Outcome: | The proposed method uses a regularly updated corpus to evaluate RAG models. |
PROTEGE: Prompt-based Diverse Question Generation from Web Articles (2023.findings-emnlp)
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| Challenge: | a popular format for knowledge bases is question-answer pairs (Q&As) specialized knowledge bases that extract and store question-annwer pairs are prevalent . |
| Approach: | They propose a framework for question generation that generates diverse questions from text . they propose 'protege' framework that can generate diverse questions using a variety of prompts . |
| Outcome: | The proposed framework improves diversity and fidelity over diverse beam search and prompt-based baselines on three public Q&A datasets. |
SkillQG: Learning to Generate Question for Reading Comprehension Assessment (2023.findings-acl)
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| Challenge: | Existing question generation systems focus on the literal nature of questions and rarely consider comprehension types of the generated questions. |
| Approach: | They propose a question generation framework with controllable comprehension types for machine reading comprehension models. |
| Outcome: | Empirical results show that SkillQG outperforms baselines in quality, relevance, and skill-controllability while showing a performance boost in downstream question answering task. |