https://github.com/xiusic/MinPrompt
MinPrompt: Graph-based Minimal Prompt Data Augmentation for Few-shot Question Answering
Papers with Code 아카이브
Question Generation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 1
- 평가표
- 11
- 보존된 평가 기록
- 42
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/xiuyu0000/new_papers_codes/tree/main/t5
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/xubuvd/guesswhat
https://github.com/xuetianci/pacit
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/yizhongw/tk-instruct
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/yizhongw/truthfulqa_reeval
https://github.com/yli-z/ml4h_are_clinical_t5_models_better_for_clinical_text
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/zhiqic/chartreader
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://huggingface.co/BAAI/Aquila-VL-2B-llava-qwen
Infinity-MM: Scaling Multimodal Performance with Large-Scale and High-Quality Instruction Data
https://huggingface.co/datasets/BAAI/Infinity-MM
Infinity-MM: Scaling Multimodal Performance with Large-Scale and High-Quality Instruction Data
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.