NEW: “Using Generative AI for Research, a Quick User’s Guide”. Start from this guide on how Generative AI can be used in multiple aspects of your research. 

Welcome to the MIDAS generative AI resource hub. This page is curated to serve researchers looking to integrate Generative AI into their work.

Generative AI models, capable of creating novel, diverse, and coherent content, are revolutionizing numerous domains. They’ve demonstrated their capabilities in fields as diverse as art, music, chemistry, drug discovery, and many more. This hub is in its initial stage, and will be updated frequently. Given the rapid advance of Generative AI, it is not possible for us to build a comprehensive collection. In addition to Generative AI overviews and a list of the most common models, we will focus on featuring examples of how Generative AI is used in research; specialized, “research-use” Generative AI models, as well as models and studies developed by U-M researchers. Please get in touch if you’d like to have your model or research study using Generative AI included in our collection.

Last Updated: December 23rd, 2023

Generative AI Overview

Generative AI in Plain English

A Brief Introduction to GenAI
Kenneth N. Reid (2023). Michigan Institute for Data Science.

What are Generative AI models?
IBM Technology

What are Large Language Models (LLMs)?
Google for Developers

What Is ChatGPT Doing … and Why Does It Work?
Stephen Wolfram (2023). Stephen Wolfram Writings.

A Very Gentle Introduction to Large Language Models Without the Hype
Mark Riedl (2023). Medium.

A Comprehensive Survey of Large Language Models

Cobus Greyling (2023), Medium.

Generative AI in Technical Terms

On the opportunities and risks of foundation models.
Rishi Bommasani, et al., arXiv preprint arXiv:2108.07258 (2021).

ChatGPT is not all you need. A State of the Art Review of large Generative AI models
Roberto Gozalo-Brizuela and Eduardo C. Garrido-Merch´an. arXiv preprint arXiv (2023)

Improving language understanding by generative pre-training.
Alec Radford, et al., (2018).

Natural Language Processing with Transformers, Revised Edition.
Lewis Tunstall, Leandro von Werra, Thomas Wolf (2022).

Attention is all you need.
Ashish Vaswani, et al., Advances in neural information processing systems 30 (2017).

Fine-tuning language models from human preferences.
Daniel M. Ziegler, et al., arXiv preprint  (2019).

Examples of General-Use and Specialized Models

When selecting a model for research, there are various data points to consider: input type, output type, commercial usage allowance, as well as level of transparency (see the Stanford Foundation Model Transparency Index to learn about metrics and the importance of transparency). Below is a selection of some of the models available showing the breadth of types of Generative AI available.

**NEW**: LM Studio allows for quick and easy installation of local LLMs from HuggingFace.

  • UM-GPT: ‘AI bot from the University of Michigan, providing information and academic assistance’
  • DALLE 3: Generates images from textual descriptions.
  • Generative NVS: A diffusion-based model for 3D-aware generative novel view synthesis from as few as a single input image.
  • GPT 4.0: Expansion from GPT-3.5, with in-built DALLE-3 access and generation. 
  • LLaMA 2: A large language model (LLM) developed by Meta AI.
  • VoxelGPT: Data organization through text prompts.

For more examples of Generative AI models, see HuggingFace – a well established comprehensive model benchmarking website.

Examples of Research Use of Generative AI

Research Using Generative AI


SafeAeroBERT: Towards a Safety-Informed Aerospace-Specific Language Model.
Sequoia Andrade et al. AIAA AVIATION 2023 Forum (2023)


Language models and cognitive automation for economic research.
Anton Korinek. National Bureau of Economic Research (2023)

Other Online Resources

Other Curated Generative AI Resource Collections

AwesomeLLMA curated list of LLMs from a technical and AI-focused perspective.

Generative AI Essentials Course – GenAI course by the University of Michigan Center for Academic Innovation.

HuggingFace LLM LeaderboardGenerative AI, and especially LLMs, are evolving rapidly. A curated list of top LLMs on a set of benchmarks can be found here.

Papers with Code – A collection of Generative AI (and non-Generative AI) papers with examples of code, datasets and more. A useful resource for seeing Generative AI applications in research.

Prompt Engineering for ChatGPT – How to apply prompt engineering effectively with ChatGPT.

Stanford Helm – “Holistic Evaluation of Language Models (HELM) is a living benchmark that aims to improve the transparency of language models.”