ChatGPT is not all you need. A State of the Art Review of large Generative AI models
By Roberto Gozalo-Brizuela, Eduardo C. Garrido-Merchán. arXiv.org, 2023
357 citations recorded by the source corpus.
During the last two years there has been a plethora of large generative models such as ChatGPT or Stable Diffusion that have been published. Concretely, these models are able to perform tasks such as being a general question and answering system or automatically creating artistic images that are revolutionizing several sectors. Consequently, the implications that these generative models have in the industry and society are enormous, as several job positions may be transformed. For example, Generative AI is capable of transforming effectively and creatively texts to images, like the DALLE-2 model; text to 3D images, like the Dreamfusion model; images to text, like the Flamingo model; texts to video, like the Phenaki model; texts to audio, like the AudioLM model; texts to other texts, like ChatGPT; texts to code, like the Codex model; texts to scientific texts, like the Galactica model or even create algorithms like AlphaTensor. This work consists on an attempt to describe in a concise way the main models are sectors that are affected by generative AI and to provide a taxonomy of the main generative models published recently.
Subfields: Health Informatics
View Paper: https://www.semanticscholar.org/paper/1f22de83d912176cb8857efa1c6d65b14d6a2f5c
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In Health Informatics
Citations in the corpus
- Generative AI in healthcare: an implementation science informed translational path on application, integration and governance (2024, background): “There are several categories of generative AI, as outlined in Table 1 [20–23].”
- From Words to Watts: Benchmarking the Energy Costs of Large Language Model Inference (2023, methodology): “We refer to [17] for a comprehensive review of the broad classes of GenAI and their capabilities.”
- Grounded SAM: Assembling Open-World Models for Diverse Visual Tasks (2024, neutral): “ChatGPT & GPT-4 [15, 46] are large language models developed using the GPT (Generative Pre-trained Trans-former) architecture, which is used for building conversational AI agents.”
- Unleashing the Power of Edge-Cloud Generative AI in Mobile Networks: A Survey of AIGC Services (2023, neutral): “MobileBERT is a highly efficient model compatible with various devices, including smartphones and IoT devices, and can be used in multiple mobile applications, such as personal assistants, chatbots, and text-to-speech systems [34].”
- On the Robustness of ChatGPT: An Adversarial and Out-of-distribution Perspective (2023, background): “Similar empirical evaluations are also made by [2, 16].”
- Self-Consuming Generative Models Go MAD (2023, background): “Companies like Google, Microsoft, and Shutterstock are incorporating generative models into their consumer services, and the output from these services and popular generative models like Stable Diffusion [2] (for images) and ChatGPT [16] (for text) tend to end up on the Internet.”
- ChatGPT and Open-AI Models: A Preliminary Review (2023, background): “Meanwhile, DALL·E is a model that generates and modifies images based on a natural language input [18].”
- Exploring the Integration of Large Language Models in Industrial Test Maintenance Processes (2024, background): “Because of their ability to understand and output both natural language and code, LLMs are well-suited for software development tasks including test generation, automated program repair, and code review [39, 45, 93, 114, 128].”