Generative AI and LLMs: Natural Language Processing and Generative Adversarial Networks

Price: $153.99
(as of Dec 24,2024 01:36:23 UTC – Details)

Publisher ‏ : ‎ De Gruyter; 1st edition (September 23, 2024)
Language ‏ : ‎ English
Hardcover ‏ : ‎ 289 pages
ISBN-10 ‏ : ‎ 3111424634
ISBN-13 ‏ : ‎ 978-3111424637
Item Weight ‏ : ‎ 1.46 pounds
Dimensions ‏ : ‎ 6.69 x 0.69 x 9.61 inches

Generative AI and LLMs: Natural Language Processing and Generative Adversarial Networks

In recent years, there has been a significant advancement in the field of natural language processing (NLP) with the development of generative artificial intelligence (AI) models such as Large Language Models (LLMs) and Generative Adversarial Networks (GANs). These models have revolutionized the way we interact with machines and have opened up new possibilities for creating human-like text and conversation.

LLMs, such as OpenAI’s GPT-3, are pre-trained language models that can generate text based on the input they receive. These models are capable of understanding and generating human-like text in a variety of languages and styles. They have been used for a wide range of applications, including chatbots, language translation, and content generation.

On the other hand, GANs are a type of deep learning model that consists of two neural networks – a generator and a discriminator – that are trained together in a competitive manner. The generator generates new data samples, while the discriminator tries to distinguish between real and generated data. This adversarial training process results in the generator producing increasingly realistic samples.

When applied to natural language processing, GANs can be used to generate text that is indistinguishable from human-written text. By combining the power of LLMs with GANs, researchers and developers are able to create AI models that can not only understand and generate text but also generate text that is highly realistic and human-like.

Overall, the combination of generative AI models like LLMs and GANs has the potential to revolutionize the field of natural language processing and bring us closer to creating truly intelligent machines that can communicate with us in a natural and human-like way.
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