https://github.com/lxchtan/tegtok
공식 코드TegTok: Augmenting Text Generation via Task-specific and Open-world Knowledge
Papers with Code 아카이브
Question Generation
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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.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/magic282/NQG
공식 코드https://github.com/mahmudhasankhan/vqg-in-bengali
공식 코드https://github.com/manestay/paxqa
공식 코드PAXQA: Generating Cross-lingual Question Answering Examples at Training Scale
https://github.com/markossta/aied_2025_video_qg
공식 코드https://github.com/meghabyte/acl2021-education
공식 코드https://github.com/mengxiayu/coser
공식 코드Context Selection and Rewriting for Video-based Educational Question Generation
https://github.com/microsoft/ProphetNet
공식 코드ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training
https://github.com/microsoft/advnlg
공식 코드Joint Generator-Ranker Learning for Natural Language Generation
https://github.com/microsoft/clarification-qgen-globalinfo
공식 코드https://github.com/microsoft/fastseq
공식 코드EL-Attention: Memory Efficient Lossless Attention for Generation
https://github.com/microsoft/unilm
공식 코드Unified Language Model Pre-training for Natural Language Understanding and Generation
https://github.com/microsoft/unilm/tree/master/deltalm
공식 코드https://github.com/mil-tokyo/vqg-unknown
공식 코드Visual Question Generation for Class Acquisition of Unknown Objects
https://github.com/moonlightlane/multiqg-ti
공식 코드MultiQG-TI: Towards Question Generation from Multi-modal Sources
https://github.com/my625/promptqg
공식 코드https://github.com/naacldemo2025/Persona-SQ-demo
공식 코드Persona-SQ: A Personalized Suggested Question Generation Framework For Real-world Documents
https://github.com/naver/aqm-plus
공식 코드Large-Scale Answerer in Questioner's Mind for Visual Dialog Question Generation
https://github.com/nbalepur/text-fact-transfer
공식 코드https://github.com/nedjmaou/varifocal_fact_checking_qg
공식 코드https://github.com/netop-team/teleqna
공식 코드TeleQnA: A Benchmark Dataset to Assess Large Language Models Telecommunications Knowledge
https://github.com/nicyscaria/AEQG-MCQ-Distractors-Physics
공식 코드https://github.com/nicyscaria/aeqg_blooms_evaluation_llms
공식 코드https://github.com/njunlp/knn-box
공식 코드kNN-BOX: A Unified Framework for Nearest Neighbor Generation
https://github.com/noagarcia/ArtVQA
공식 코드A Dataset and Baselines for Visual Question Answering on Art
https://github.com/nycu-nlp-lab/e-qgen
공식 코드E-QGen: Educational Lecture Abstract-based Question Generation System
https://github.com/nyu-dl/dl4mt-cdec
공식 코드Plan, Attend, Generate: Planning for Sequence-to-Sequence Models
https://github.com/obss/turkish-question-generation
공식 코드Automated question generation and question answering from Turkish texts
https://github.com/olivia-fsm/p2mcq
공식 코드Towards Process-Oriented, Modular, and Versatile Question Generation that Meets Educational Needs
https://github.com/ozyyshr/oscar
공식 코드Smoothing Dialogue States for Open Conversational Machine Reading
https://github.com/panushri25/emrQA
공식 코드CliniQG4QA: Generating Diverse Questions for Domain Adaptation of Clinical Question Answering
https://github.com/pavanneerudu/robustness-of-transformers-models
공식 코드https://github.com/primate-mh/primate2022
공식 코드https://github.com/princeton-nlp/DensePhrases
공식 코드https://github.com/princeton-nlp/carets
공식 코드CARETS: A Consistency And Robustness Evaluative Test Suite for VQA
https://github.com/probe2/multi-hop
공식 코드MINTQA: A Multi-Hop Question Answering Benchmark for Evaluating LLMs on New and Tail Knowledge
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.