26 datasets found

Product descriptions on e-commerce websites often suffer from missing important aspects. Clarification question generation (CQ-Gen) can be a promising approach to help alleviate...

The dataset is used to train the GlyphDraw model for visual text generation. It contains 792k images with 3.3M characters in images and more than 4.8k common unique Chinese...

Linear-time minimum Bayes risk decoding with reference aggregation

BERTScore: Evaluating text generation with BERT

Multi-prompt decoding for conditional text generation

TextLogo3K dataset is a large-scale dataset of text logos, consisting of 3,470 text logo images with various styles and annotated with pixel-level segmentation, bounding boxes,...

Reference letter dataset generated under the Context-Based Generation (CBG) setting.

A dataset of AI Wiki, used for testing the author-stylized text generation model.

A dataset of Mark Twain's books, used for testing the author-stylized text generation model.

A dataset of Opinosis Review dataset, used for testing the author-stylized text generation model.

The dataset used in the paper is a subset of the Wikipedia corpus, consisting of 7500 English Wikipedia articles belonging to one of the following categories: People, Cities,...

A dataset of 2,857 books written by 141 authors, used for pre-training and fine-tuning a language model for author-stylized text generation.

ChatGPT model data, used to generate text

A vision-language pre-training dataset, BLIP2, which consists of 100 million image-text pairs.

Diffusion models have emerged as a power-ful paradigm for generation, obtaining strong performance in various continuous domains. However, applying continuous diffusion models...

The dataset used for pre-training language models, containing a large collection of text documents.

Text-to-image generation via masked generative transformers.

A dataset for language modeling, where the goal is to predict the next word in a sequence given the previous words.

A dataset for language modeling, where the goal is to predict the next word in a sequence given the previous words.

Commonsense generation aims to generate a realistic sentence describing a daily scene under the given concepts, which is very challenging, since it requires models to have...