A text generation model is a machine learning model that uses neural networks, especially transformers architecture to generate contextually relevant text based on linguistic patterns learned from extensive corpora. The models are trained on a huge amount of textual data so that they can model and learn complex concepts of any language like its grammar, vocabulary, phrases, and styles. Text generation models can increase the productivity of humans in their current business processes. These models are already automating the process of content creation across industries for the generation of reports, summaries, and emails among others. These models are also allowing for a greater level of personalization in communications between businesses and their customers.

CNet's AI SNFAU turned out to be merely the first pebble kicked down the slippery slope. In a Thursday morning internal memo acquired by the Wall Street Journal, Buzzfeed Chief Executive Jonah Peretti announced plans to embrace AI in both editorial and business operations and utilize text generation systems similar to CNet's to produce, for example, the memeable quizzes that originally built Buzzfeed's following. Such AI-powered quizzes could provide more personalized answers based on the user's more specific responses rather than based on a score range or ranked choice system like they are today. Peretti envisions AI not only producing content on its own but drawing inspiration from human writers. We squishy meat sacks would serve as idea sources for AI text generators, or as Peretti described members of his own species, "cultural currency" and "inspired prompts."

A major challenge in the field of Text Generation is evaluation because we lack a sound theory that can be leveraged to extract guidelines for evaluation campaigns. In this work, we propose a first step towards such a theory that incorporates different sources of uncertainty, such as imperfect automated metrics and insufficiently sized test sets. The theory has practical applications, such as determining the number of samples needed to reliably distinguish the performance of a set of Text Generation systems in a given setting. We showcase the application of the theory on the WMT 21 and Spot-The-Bot evaluation data and outline how it can be leveraged to improve the evaluation protocol regarding the reliability, robustness, and significance of the evaluation outcome.

Text generation is the task of automatically generating text using a machine learning system. A good text generation system can make it really hard to distinguish between human and machine-written text pieces.

Text generation system has made massive promising progress contributed by deep learning techniques and has been widely applied in our life. However, existing end-to-end neural models suffer from the problem of tending to generate uninformative and generic text because they cannot ground input context with background knowledge. In order to solve this problem, many researchers begin to consider combining external knowledge in text generation systems, namely knowledge-enhanced text generation. The challenges of knowledge enhanced text generation including how to select the appropriate knowledge from large-scale knowledge bases, how to read and understand extracted knowledge, and how to integrate knowledge into generation process. This survey gives a comprehensive review of knowledge-enhanced text generation systems, summarizes research progress to solving these challenges and proposes some open issues and research directions.