Text Generation

1453 papers with code • 167 benchmarks • 149 datasets

Text Generation is the task of generating text with the goal of appearing indistinguishable to human-written text. This task if 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:

( Image credit: Adversarial Ranking for Language Generation )

Libraries

Use these libraries to find Text Generation models and implementations
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Latest papers with no code

Improving Attributed Text Generation of Large Language Models via Preference Learning

no code yet • 27 Mar 2024

Large language models have been widely adopted in natural language processing, yet they face the challenge of generating unreliable content.

Scaling Laws For Dense Retrieval

no code yet • 27 Mar 2024

In this study, we investigate whether the performance of dense retrieval models follows the scaling law as other neural models.

SciNews: From Scholarly Complexities to Public Narratives -- A Dataset for Scientific News Report Generation

no code yet • 26 Mar 2024

Scientific news reports serve as a bridge, adeptly translating complex research articles into reports that resonate with the broader public.

Language Models for Text Classification: Is In-Context Learning Enough?

no code yet • 26 Mar 2024

This makes them suitable for addressing text classification problems for domains with limited amounts of annotated instances.

MapGuide: A Simple yet Effective Method to Reconstruct Continuous Language from Brain Activities

no code yet • 26 Mar 2024

In contrast, we propose a simple yet effective method that guides text reconstruction by directly comparing them with the predicted text embeddings mapped from brain activities.

The Solution for the ICCV 2023 1st Scientific Figure Captioning Challenge

no code yet • 26 Mar 2024

In this paper, we propose a solution for improving the quality of captions generated for figures in papers.

Automated Report Generation for Lung Cytological Images Using a CNN Vision Classifier and Multiple-Transformer Text Decoders: Preliminary Study

no code yet • 26 Mar 2024

Independent text decoders for benign and malignant cells are prepared for text generation, and the text decoder switches according to the CNN classification results.

DORE: A Dataset For Portuguese Definition Generation

no code yet • 26 Mar 2024

In this research, we fill this gap by introducing DORE; the first dataset for Definition MOdelling for PoRtuguEse containing more than 100, 000 definitions.

Dia-LLaMA: Towards Large Language Model-driven CT Report Generation

no code yet • 25 Mar 2024

Medical report generation has achieved remarkable advancements yet has still been faced with several challenges.