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 376400 of 664 papers

TitleStatusHype
QuOTE: Question-Oriented Text Embeddings0
QURIOUS: Question Generation Pretraining for Text Generation0
RAGAR, Your Falsehood Radar: RAG-Augmented Reasoning for Political Fact-Checking using Multimodal Large Language Models0
Ranking Model with a Reduced Feature Set for an Automated Question Generation System0
Reasoning Circuits: Few-shot Multihop Question Generation with Structured Rationales0
Recent Advances in Neural Question Generation0
Reference-based Metrics Disprove Themselves in Question Generation0
Reframing Instructional Prompts to GPTk's Language0
Reinforced Multi-task Approach for Multi-hop Question Generation0
Resolving Intent Ambiguities by Retrieving Discriminative Clarifying Questions0
Restatement and Question Generation for Counsellor Chatbot0
Rethinking the Agreement in Human Evaluation Tasks0
Retrieval-guided Counterfactual Generation for QA0
Retrieval-guided Counterfactual Generation for QA0
Review-based Question Generation with Adaptive Instance Transfer and Augmentation0
RevUP: Automatic Gap-Fill Question Generation from Educational Texts0
RV-Syn: Rational and Verifiable Mathematical Reasoning Data Synthesis based on Structured Function Library0
s2s-ft: Fine-Tuning Pretrained Transformer Encoders for Sequence-to-Sequence Learning0
Savaal: Scalable Concept-Driven Question Generation to Enhance Human Learning0
Selecting Better Samples from Pre-trained LLMs: A Case Study on Question Generation0
Selecting Domain-Specific Concepts for Question Generation With Lightly-Supervised Methods0
Self-Attention Architectures for Answer-Agnostic Neural Question Generation0
Self Rewarding Self Improving0
Self-supervised clarification question generation for ambiguous multi-turn conversation0
Self-Training for Jointly Learning to Ask and Answer Questions0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ERNIE-GENLARGE (beam size=5)BLEU-425.41Unverified
2BART (TextBox 2.0)BLEU-425.08Unverified
3ProphetNet + ASGenBLEU-424.44Unverified
4UniLMv2BLEU-424.43Unverified
5ProphetNet + syn. mask + localnessBLEU-424.37Unverified
6ProphetNetBLEU-423.91Unverified
7UniLM + ASGenBLEU-423.7Unverified
8UniLMBLEU-422.78Unverified
9BERTSQGBLEU-422.17Unverified
10Selector & NQG++BLEU-415.87Unverified
#ModelMetricClaimedVerifiedStatus
1MDNBLEU-165.1Unverified
2coco-Caption [[Karpathy and Li2014]]BLEU-162.5Unverified
3Max(Yang,2015)BLEU-159.4Unverified
4Sample(Yang,2015)BLEU-138.8Unverified
#ModelMetricClaimedVerifiedStatus
1FactJointGTMETEOR36.21Unverified
2JointGTMETEOR36.08Unverified
3FactT5BMETEOR35.72Unverified
4T5BMETEOR35.64Unverified
#ModelMetricClaimedVerifiedStatus
1FactT5BBLEU46.1Unverified
2JointGTBLEU45.95Unverified
3T5BBLEU44.51Unverified
4FactJointGTBLEU43.61Unverified
#ModelMetricClaimedVerifiedStatus
1JointGTMETEOR37.69Unverified
2FactJointGTMETEOR37.55Unverified
3FactT5BMETEOR37.39Unverified
4T5BMETEOR37.35Unverified
#ModelMetricClaimedVerifiedStatus
1BART fine-tuned on FairytaleQAROUGE-L0.53Unverified
2BART fine-tuned on NarrativeQA and FairytaleQAROUGE-L0.52Unverified
3BART fine-tuned on NarrativeQAROUGE-L0.44Unverified
#ModelMetricClaimedVerifiedStatus
1UniPollROUGE-149.6Unverified
2T5ROUGE-144.46Unverified
3Dual DecROUGE-138.24Unverified
#ModelMetricClaimedVerifiedStatus
1Info-HCVAEQAE37.18Unverified
2HCVAEQAE31.45Unverified
#ModelMetricClaimedVerifiedStatus
1Info-HCVAEQAE71.18Unverified
2HCVAEQAE69.46Unverified
#ModelMetricClaimedVerifiedStatus
1Info-HCVAEQAE35.45Unverified
2HCVAEQAE30.2Unverified
#ModelMetricClaimedVerifiedStatus
1MDNBLEU-136Unverified