https://github.com/hadifar/question-generation
EduQG: A Multi-format Multiple Choice Dataset for the Educational Domain
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
Text Generation is the task of generating text with the goal of appearing indistinguishable to human-written text. This task is more formally known as "natural language generation" in the literature.
Text generation can be addressed with Markov processes or deep generative models like LSTMs. Recently, some of the most advanced methods for text generation include BART, GPT and other GAN-based approaches. Text generation systems are evaluated either through human ratings or automatic evaluation metrics like METEOR, ROUGE, and BLEU.
Further readings:
<span style="color:grey; opacity: 0.6">( Image credit: Adversarial Ranking for Language Generation )</span>
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
EduQG: A Multi-format Multiple Choice Dataset for the Educational Domain
Code Generation with AlphaCodium: From Prompt Engineering to Flow Engineering
BatGPT: A Bidirectional Autoregessive Talker from Generative Pre-trained Transformer
Neural Machine Translation by Jointly Learning to Align and Translate
Paying More Attention to Image: A Training-Free Method for Alleviating Hallucination in LVLMs
Neural Machine Translation by Jointly Learning to Align and Translate
LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models
Long and Diverse Text Generation with Planning-based Hierarchical Variational Model
Personalizing Dialogue Agents: I have a dog, do you have pets too?
ZeroGen: Efficient Zero-shot Learning via Dataset Generation
CoMM: A Coherent Interleaved Image-Text Dataset for Multimodal Understanding and Generation
Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models
Neural Machine Translation by Jointly Learning to Align and Translate
Data-Driven Approach to Encoding and Decoding 3-D Crystal Structures
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.