Conditional Text Generation
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Benchmarks
Lipogram-e
Most implemented
GENIUS: Sketch-based Language Model Pre-training via Extreme and Selective Masking for Text Generation and Augmentation
The Dialog Must Go On: Improving Visual Dialog via Generative Self-Training
BanglaNLG and BanglaT5: Benchmarks and Resources for Evaluating Low-Resource Natural Language Generation in Bangla
Extract, Denoise and Enforce: Evaluating and Improving Concept Preservation for Text-to-Text Generation
Unifying Vision-and-Language Tasks via Text Generation
Pragmatically Informative Text Generation
Papers
UTDesign: A Unified Framework for Stylized Text Editing and Generation in Graphic Design Images
AI-assisted graphic design has emerged as a powerful tool for automating the creation and editing of design elements such as posters, banners, and advertisements. While diffusion-based text-to-image models have demonstra…
Conditional Text GenerationText Style TransferACTG-ARL: Differentially Private Conditional Text Generation with RL-Boosted Control
Generating high-quality synthetic text under differential privacy (DP) is critical for training and evaluating language models without compromising user privacy. Prior work on synthesizing DP datasets often fail to prese…
Conditional Text GenerationGenerating Surface for Text-to-3D using 2D Gaussian Splatting
Recent advancements in Text-to-3D modeling have shown significant potential for the creation of 3D content. However, due to the complex geometric shapes of objects in the natural world, generating 3D content remains a ch…
Conditional Text GenerationSyntax-Guided Diffusion Language Models with User-Integrated Personalization
Large language models have made revolutionary progress in generating human-like text, yet their outputs often tend to be generic, exhibiting insufficient structural diversity, which limits personalized expression. Recent…
Conditional Text GenerationCtrlDiff: Boosting Large Diffusion Language Models with Dynamic Block Prediction and Controllable Generation
Although autoregressive models have dominated language modeling in recent years, there has been a growing interest in exploring alternative paradigms to the conventional next-token prediction framework. Diffusion-based l…
Conditional Text GenerationLanguage ModelingLanguage ModellingText GenerationSCOPE: A Self-supervised Framework for Improving Faithfulness in Conditional Text Generation
Large Language Models (LLMs), when used for conditional text generation, often produce hallucinations, i.e., information that is unfaithful or not grounded in the input context. This issue arises in typical conditional t…
Conditional Text GenerationData-to-Text GenerationText GenerationText Summarization