SOTAVerified

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

Showing 110 of 664 papers

TitleStatusHype
Compressed and Smooth Latent Space for Text Diffusion Modeling0
ELLIS Alicante at CQs-Gen 2025: Winning the critical thinking questions shared task: LLM-based question generation and selection0
Knowledge Compression via Question Generation: Enhancing Multihop Document Retrieval without Fine-tuning0
Multiple-Choice Question Generation Using Large Language Models: Methodology and Educator Insights0
TO-GATE: Clarifying Questions and Summarizing Responses with Trajectory Optimization for Eliciting Human Preference0
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 Models0
KG-QAGen: A Knowledge-Graph-Based Framework for Systematic Question Generation and Long-Context LLM EvaluationCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MDNBLEU-165.1Unverified
2coco-Caption [[Karpathy and Li2014]]BLEU-162.5Unverified
3Max(Yang,2015)BLEU-159.4Unverified
4Sample(Yang,2015)BLEU-138.8Unverified