SOTAVerified

Conditional Text Generation

The task of generating text according to some pre-specified conditioning (e.g. topic or sentiment or constraint)

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1–25 of 67 papers

TitleStatusHype
MEDITRON-70B: Scaling Medical Pretraining for Large Language ModelsCode4
VALOR: Vision-Audio-Language Omni-Perception Pretraining Model and DatasetCode2
Extract, Denoise and Enforce: Evaluating and Improving Concept Preservation for Text-to-Text GenerationCode1
Plug and Play Autoencoders for Conditional Text GenerationCode1
Controllable Data Augmentation for Few-Shot Text Mining with Chain-of-Thought Attribute ManipulationCode1
BanglaNLG and BanglaT5: Benchmarks and Resources for Evaluating Low-Resource Natural Language Generation in BanglaCode1
ToTTo: A Controlled Table-To-Text Generation DatasetCode1
AdaVAE: Exploring Adaptive GPT-2s in Variational Auto-Encoders for Language ModelingCode1
LOCOST: State-Space Models for Long Document Abstractive SummarizationCode1
On Long-Tailed Phenomena in Neural Machine TranslationCode1
Is Everything in Order? A Simple Way to Order SentencesCode1
Data-to-text Generation by Splicing Together Nearest NeighborsCode1
Learning to Generate Better Than Your LLMCode1
Regression Transformer: Concurrent sequence regression and generation for molecular language modelingCode1
Unifying Vision-and-Language Tasks via Text GenerationCode1
GENIUS: Sketch-based Language Model Pre-training via Extreme and Selective Masking for Text Generation and AugmentationCode1
TEncDM: Understanding the Properties of the Diffusion Model in the Space of Language Model EncodingsCode1
The Dialog Must Go On: Improving Visual Dialog via Generative Self-TrainingCode1
Contrastive Learning with Adversarial Perturbations for Conditional Text GenerationCode1
EEL: Efficiently Encoding Lattices for RerankingCode0
Pre-train and Plug-in: Flexible Conditional Text Generation with Variational Auto-EncodersCode0
Generating Text through Adversarial Training using Skip-Thought VectorsCode0
Semantic Sensitivities and Inconsistent Predictions: Measuring the Fragility of NLI ModelsCode0
Exploring Conditional Text Generation for Aspect-Based Sentiment AnalysisCode0
Evaluation of Language Models in the Medical Context Under Resource-Constrained SettingsCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1GPT-2-no-fine-tuningIgnored Constraint Error Rate28.2—Unverified
2GPT-2-fine-tuned-5-epochsIgnored Constraint Error Rate0.5—Unverified
3GPT-2-fine-tuned-20-epochsIgnored Constraint Error Rate0.3—Unverified
4GPT-2-with-filterIgnored Constraint Error Rate0—Unverified