Text Generation
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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>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/zyxElsa/IP-Prompter
공식 코드IP-Prompter: Training-Free Theme-Specific Image Generation via Dynamic Visual Prompting
https://github.com/zyzhangustc/mellm
공식 코드MELLM: Exploring LLM-Powered Micro-Expression Understanding Enhanced by Subtle Motion Perception
https://github.com/zzbuzzard/guidediffuseq
공식 코드Understanding the Quality-Diversity Trade-off in Diffusion Language Models
https://github.com/zzsfornlp/zgen1
공식 코드Towards More Efficient Insertion Transformer with Fractional Positional Encoding
https://gitlab.com/bpaassen/rtgae
공식 코드https://gitlab.com/webnlg/webnlg-human-evaluation
공식 코드Human vs Automatic Metrics: on the Importance of Correlation Design
https://huggingface.co/Akila/ForgottenRealmsFreeTextGenerator
공식 코드Synthesis and Evaluation of a Domain-specific Large Data Set for Dungeons & Dragons
https://huggingface.co/datasets/facebook/content_rephrasing
공식 코드https://huggingface.co/datasets/jdustinwind/Polite
공식 코드Pay Attention to Your Tone: Introducing a New Dataset for Polite Language Rewrite
https://huggingface.co/nevmenandr/char-based-lstm-russian-poetry-pasternak
공식 코드Identifying the style by a qualified reader on a short fragment of generated poetry
https://worksheets.codalab.org/worksheets/0x5a7d0fe35b144ad68998d74891a31ed6
공식 코드https://worksheets.codalab.org/worksheets/0x88644b5ee189402eb19d39d721d1005c
공식 코드Unifying Human and Statistical Evaluation for Natural Language Generation
https://worksheets.codalab.org/worksheets/0x9987b5d9cce74cf4b2a5f84b54ee447b
공식 코드https://worksheets.codalab.org/worksheets/0xe3eb416773ed4883bb737662b31b4948
공식 코드Delete, Retrieve, Generate: A Simple Approach to Sentiment and Style Transfer
https://git.zib.de/ableich/ecg-comment-generation-public
https://github.com/04RR/SOTA-Vision
https://github.com/10-zin/Synthesizer
Synthesizer: Rethinking Self-Attention in Transformer Models
https://github.com/1171-jpg/brainteaser
BRAINTEASER: Lateral Thinking Puzzles for Large Language Models
https://github.com/1wy/bert
https://github.com/20000607-lxc/TransformerCVAE-master
Transformer-based Conditional Variational Autoencoder for Controllable Story Generation
https://github.com/2023-MindSpore-1/ms-code-14/tree/main/Pangu_alpha
https://github.com/2023-MindSpore-1/ms-code-14/tree/main/Transformer
https://github.com/2023-MindSpore-1/ms-code-149
https://github.com/2023-MindSpore-1/ms-code-154
https://github.com/2023-MindSpore-1/ms-code-162
https://github.com/2023-MindSpore-1/ms-code-200
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/2023-MindSpore-1/ms-code-219/tree/main/duconv
Multiple Generative Models Ensemble for Knowledge-Driven Proactive Human-Computer Dialogue Agent
https://github.com/2023-MindSpore-4/Code10/tree/main/DeepText
https://github.com/2023-MindSpore-4/Code10/tree/main/gan
https://github.com/2023-MindSpore-4/Code12/tree/main/MindFormers/clip
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/2023-MindSpore-4/Code12/tree/main/MindFormers/pangualpha
PanGu-Bot: Efficient Generative Dialogue Pre-training from Pre-trained Language Model
https://github.com/2023-MindSpore-4/Code12/tree/main/ganleilei/L2TviaCounterfactualSamples-main-main
https://github.com/2023-MindSpore-4/Code8/tree/main/AttentionCluster
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/audio_spectrogram_transformer
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/bart
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.