Papers with Code 아카이브
Conditional Text Generation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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The task of generating text according to some pre-specified conditioning (e.g. topic or sentiment or constraint)
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/ImKeTT/adavae
공식 코드AdaVAE: Exploring Adaptive GPT-2s in Variational Auto-Encoders for Language Modeling
https://github.com/M0RJIQUE/tencdm
공식 코드TEncDM: Understanding the Properties of the Diffusion Model in the Space of Language Model Encodings
https://github.com/Sampreeth-sarma/T5-ABSA-Summarization
공식 코드Exploring Conditional Text Generation for Aspect-Based Sentiment Analysis
https://github.com/TXH-mercury/VALOR
공식 코드VALOR: Vision-Audio-Language Omni-Perception Pretraining Model and Dataset
https://github.com/WHUIR/PPVAE
공식 코드Pre-train and Plug-in: Flexible Conditional Text Generation with Variational Auto-Encoders
https://github.com/Xiaomeng-Yang/IPAD
공식 코드IPAD: Iterative, Parallel, and Diffusion-based Network for Scene Text Recognition
https://github.com/ZhaoqLiu/BreakNLI
공식 코드Semantic Sensitivities and Inconsistent Predictions: Measuring the Fragility of NLI Models
https://github.com/afrozas/skip-thought-gan
공식 코드Generating Text through Adversarial Training using Skip-Thought Vectors
https://github.com/anpoc/language-models-in-medicine
공식 코드Evaluation of Language Models in the Medical Context Under Resource-Constrained Settings
https://github.com/beyondguo/genius
공식 코드https://github.com/cornell-rl/tril
공식 코드https://github.com/csebuetnlp/banglanlg
공식 코드https://github.com/deepmind/deepmind-research/tree/master/wikigraphs
공식 코드WikiGraphs: A Wikipedia Text - Knowledge Graph Paired Dataset
https://github.com/eaglew/clbd
공식 코드SciMON: Scientific Inspiration Machines Optimized for Novelty
https://github.com/epfllm/meditron
공식 코드MEDITRON-70B: Scaling Medical Pretraining for Large Language Models
https://github.com/fabrahman/rebart
공식 코드https://github.com/flbbb/locost-summarization
공식 코드LOCOST: State-Space Models for Long Document Abstractive Summarization
https://github.com/florianmai/emb2emb
공식 코드https://github.com/fyyfu/semantic-watermark
공식 코드https://github.com/gicheonkang/gst-visdial
공식 코드The Dialog Must Go On: Improving Visual Dialog via Generative Self-Training
https://github.com/google-research-datasets/ToTTo
공식 코드https://github.com/gsarti/ETC-NLG
공식 코드ETC-NLG: End-to-end Topic-Conditioned Natural Language Generation
https://github.com/harvardnlp/encoder-agnostic-adaptation
공식 코드Encoder-Agnostic Adaptation for Conditional Language Generation
https://github.com/hellisotherpeople/constrained-text-generation-studio
공식 코드https://github.com/ibm/regression-transformer
공식 코드https://github.com/j-min/VL-T5
공식 코드https://github.com/komeijiforce/cotam
공식 코드Controllable Data Augmentation for Few-Shot Text Mining with Chain-of-Thought Attribute Manipulation
https://github.com/microsoft/scglab
공식 코드https://github.com/morningmoni/LCGen-eval
공식 코드https://github.com/morningmoni/ede
공식 코드https://github.com/prasanns/eel-reranking
공식 코드https://github.com/seanie12/CLAPS
공식 코드Contrastive Learning with Adversarial Perturbations for Conditional Text Generation
https://github.com/swiseman/neighbor-splicing
공식 코드Data-to-text Generation by Splicing Together Nearest Neighbors
https://github.com/twidddj/tokmangan
공식 코드Token Manipulation Generative Adversarial Network for Text Generation
https://github.com/vyraun/long-tailed
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.