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Question Generation

The goal of Question Generation is to generate a valid and fluent question according to a given passage and the target answer. Question Generation can be used in many scenarios, such as automatic tutoring systems, improving the performance of Question Answering models and enabling chatbots to lead a conversation.

Source: Generating Highly Relevant Questions

Papers

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Showing 1–10 of 664 papers

TitleStatusHype
Compressed and Smooth Latent Space for Text Diffusion Modeling—0
ELLIS Alicante at CQs-Gen 2025: Winning the critical thinking questions shared task: LLM-based question generation and selection—0
Knowledge Compression via Question Generation: Enhancing Multihop Document Retrieval without Fine-tuning—0
Multiple-Choice Question Generation Using Large Language Models: Methodology and Educator Insights—0
TO-GATE: Clarifying Questions and Summarizing Responses with Trajectory Optimization for Eliciting Human Preference—0
Bench4KE: Benchmarking Automated Competency Question GenerationCode1
Ask, Retrieve, Summarize: A Modular Pipeline for Scientific Literature SummarizationCode0
Multi-Hop Question Generation via Dual-Perspective Keyword GuidanceCode0
AutoGEEval: A Multimodal and Automated Framework for Geospatial Code Generation on GEE with Large Language Models—0
KG-QAGen: A Knowledge-Graph-Based Framework for Systematic Question Generation and Long-Context LLM EvaluationCode0
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All datasetsSQuAD1.1COCO Visual Question Answering (VQA) real images 1.0 open endedGrailQA-CompositionalGrailQA-IIDGrailQA-Zero-ShotFairytaleQAWeiboPollsNatural QuestionsSQuADTriviaQAVisual Question Generation

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1FactJointGTMETEOR36.21—Unverified
2JointGTMETEOR36.08—Unverified
3FactT5BMETEOR35.72—Unverified
4T5BMETEOR35.64—Unverified