A list of resources, articles, and opinion pieces relating to generative AI models – September 2024 update

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Image by Teresa Berndtsson / Better Images of AI / Letter Word Text Taxonomy / Licensed by CC-BY 4.0.

Introduction

We have curated a collection of articles, opinion pieces, videos, and resources related to generative AI models. This list is periodically updated to include new and relevant resources. This article is the fourth in the series. You can find the previous versions here: v1, v2, and v3.

Tutorials, Explainers, and Courses on Generative Models

  1. What are Generative AI models? – Video by Kate Soule, IBM Technology.
  2. Introduction to Large Language Models – Video by John Ewald, Google Cloud Tech.
  3. What is GPT-4 and how does it differ from ChatGPT? – Alex Hern, The Guardian.
  4. What Is ChatGPT Doing … and Why Does It Work? – Stephen Wolfram.
  5. Understanding Large Language Models — A Transformative Reading List – Sebastian Raschka.
  6. How ChatGPT is Trained – Video by Ari Seff.
  7. ChatGPT – what is it? How does it work? Should we be excited? Or scared? – Deep Dhillon, The Radical AI podcast.
  8. Everything you need to know about ChatGPT – Joanna Dungate, Turing Institute Blog.
  9. Turing video lecture series on foundation models: Session 1 | Session 2 | Session 3 | Session 4.
  10. Bard: What is Google’s Bard and how is it different to ChatGPT? – BBC.
  11. Bard FAQs – Google.
  12. Large Language Models from scratch | Large Language Models: Part 2* – Videos from Graphics in 5 minutes*.
  13. What are Large Language Models (LLMs)? – Video from Google for Developers.
  14. Risks of Large Language Models (LLM) – Phaedra Boinodiris, video from IBM Technology.
  15. How ChatGPT and Other LLMs Work—and Where They Could Go Next – David Nield, Wired.
  16. What are Large Language Models – Machine Learning Mastery.
  17. How To Delete Your Data From ChatGPT – Matt Burgess, Wired.
  18. 5 Ways ChatGPT Can Improve, Not Replace, Your Writing – David Nield, Wired.
  19. AI prompt engineering: learn how not to ask a chatbot a silly question – Callum Bains, The Guardian.
  20. How to tell if an image is AI-generated – The Guardian.
  21. GPT-4 – How does it work, and how do I build apps with it? – CS50 Tech Talk.
  22. Developing an LLM: Building, Training, Finetuning – Tutorial video from Sebastian Raschka.
  23. Finetuning Open-Source LLMs – Tutorial video from Sebastian Raschka.
  24. Building a LLM from scratch – Tutorial video from Sebastian Raschka.
  25. Generative AI for Beginners – A Course – Microsoft.
  26. What is generative AI? – IBM.
  27. Using generative AI to write code: a guide for researchers – The Alan Turing Institute.
  28. Introduction to Generative AI – Google.

    Journal, Conference, arXiv, and Other Articles

  29. Scientists’ Perspectives on the Potential for Generative AI in their Fields – Meredith Ringel Morris, arXiv.
  30. LaMDA: Language Models for Dialog Applications – Romal Thoppilan et al., arXiv.
  31. What Language Model to Train if You Have One Million GPU Hours? – Teven Le Scao et al., arXiv.
  32. Alpaca: A Strong, Replicable Instruction-Following Model – Rohan Taori et al.
  33. Process for Adapting Language Models to Society (PALMS) with Values-Targeted Datasets – Irene Solaiman, Christy Dennison, NeurIPS 2021.
  34. On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? – Emily Bender, Timnit Gebru, Angelina McMillan-Major, Shmargaret Shmitchell, FAccT 2021.
  35. A Survey of Large Language Models – Wayne Xin Zhao et al., arXiv.
  36. A Watermark for Large Language Models – John Kirchenbauer, Jonas Geiping, Yuxin Wen, Jonathan Katz, Ian Miers, Tom Goldstein, arXiv.
  37. Between Subjectivity and Imposition: Power Dynamics in Data Annotation for Computer Vision – Milagros Miceli, Martin Schuessler, Tianling Yang, Proceedings of the ACM on Human-Computer Interaction.
  38. AI classifier for indicating AI-written text – OpenAI.
  39. Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling – Stella Biderman et al., arXiv.
  40. GPT-4 Technical Report – OpenAI, arXiv.
  41. GPT-4 System Card – OpenAI.
  42. BloombergGPT: A Large Language Model for Finance – Shijie Wu et al., arXiv.
  43. Evading Watermark based Detection of AI-Generated Content – Zhengyuan Jiang, Jinghuai Zhang, Neil Zhenqiang Gong, arXiv.
  44. PaLM 2 Technical Report – Google.
  45. Large language models (LLM) and ChatGPT: what will the impact on nuclear medicine be? – Ian L. Alberts, Lorenzo Mercolli, Thomas Pyka, George Prenosil, Kuangyu Shi, Axel Rominger, and Ali Afshar-Oromieh, Eur J Nucl Med Mol Imaging.
  46. Ethics of large language models in medicine and medical research – Hanzhou Li, John T Moon, Saptarshi Purkayastha, Leo Anthony Celi, Hari Trivedi and Judy W Gichoya, The Lancet.
  47. Science in the age of large language models – Abeba Birhane, Atoosa Kasirzadeh, David Leslie & Sandra Wachter, Nature.
  48. Standardizing chemical compounds with language models – Miruna T Cretu, Alessandra Toniato, Amol Thakkar, Amin A Debabeche, Teodoro Laino and Alain C Vaucher, Machine Learning: Science and Technology.
  49. How to keep text private? A systematic review of deep learning methods for privacy-preserving natural language processing – Samuel Sousa & Roman Kern, Artificial Intelligence Review.
  50. Material transformers: deep learning language models for generative materials design – Nihang Fu, Lai Wei, Yuqi Song, Qinyang Li, Rui Xin, Sadman Sadeed Omee, Rongzhi Dong, Edirisuriya M Dilanga Siriwardane and Jianjun Hu, Machine Learning: Science and Technology.
  51. Large language models encode clinical knowledge – Karan Singhal et al., Nature.
  52. BLOOM: A 176B-Parameter Open-Access Multilingual Language Model – Teven Le Scao et al., arXiv.
  53. SELFormer: molecular representation learning via SELFIES language models – Atakan Yüksel, Erva Ulusoy, Atabey Ünlü and Tunca Doğan, Machine Learning: Science and Technology.
  54. Are Emergent Abilities of Large Language Models a Mirage? – Rylan Schaeffer, Brando Miranda, Sanmi Koyejo, NeurIPS 2023.
  55. Scaling Data-Constrained Language Models – Niklas Muennighoff, Alexander Rush, Boaz Barak, Teven Le Scao, Nouamane Tazi, Aleksandra Piktus, Sampo Pyysalo, Thomas Wolf, Colin Raffel, NeurIPS 2023.
  56. DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models – Boxin Wang, Weixin Chen, Hengzhi Pei, Chulin Xie, Mintong Kang, Chenhui Zhang, Chejian Xu, Zidi Xiong, Ritik Dutta, Rylan Schaeffer, Sang Truong, Simran Arora, Mantas Mazeika, Dan Hendrycks, Zinan Lin, Yu Cheng, Sanmi Koyejo, Dawn Song, Bo Li, NeurIPS 2023.
  57. A Watermark for Large Language Models – John Kirchenbauer, Jonas Geiping, Yuxin Wen, Jonathan Katz, Ian Miers, Tom Goldstein, ICML 2023.
  58. Foundation Models for Music: A Survey – Yinghao Ma et al., arXiv.
  59. Generative AI: A systematic review using topic modelling techniques – Priyanka Gupta, Bosheng Ding, Chong Guan, Ding Ding, Data and Information Management.
  60. Gemini: A Family of Highly Capable Multimodal Models – Rohan Anil et al., arXiv.
  61. Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context – Petko Georgiev et al., arXiv.
  62. The Llama 3 Herd of Models – Abhimanyu Dubey et al., arXiv.
  63. Monitoring AI-Modified Content at Scale: A Case Study on the Impact of ChatGPT on AI Conference Peer Reviews – Weixin Liang, Zachary Izzo, Yaohui Zhang, Haley Lepp, Hancheng Cao, Xuandong Zhao, Lingjiao Chen, Haotian Ye, Sheng Liu, Zhi Huang, Daniel A. McFarland, James Y. Zou, arXiv.
  64. Delving into ChatGPT usage in academic writing through excess vocabulary – Dmitry Kobak, Rita González-Márquez, Emőke-Ágnes Horvát, Jan Lause, arXiv.

    Blog Posts and Interviews About Published Scientific Papers

  65. GPT-4 + Stable-Diffusion = ?: Enhancing Prompt Understanding of Text-to-Image Diffusion Models with Large Language Models – Long Lian, Boyi Li, Adam Yala, and Trevor Darrell, BAIR blog.
  66. Interview with Bo Li: A comprehensive assessment of trustworthiness in GPT models – AIhub.
  67. Interview with Changhoon Kim – enhancing the reliability of image generative AI – AIhub.
  68. Utilizing generative adversarial networks for stable structure generation in Angry Birds – Matthew Stephenson and Frederic Abraham, AIhub.
  69. Riemannian score-based generative modelling – Valentin De Bortoli, AIhub.
  70. **[Interview with Paula Feldman: generating 3d models of blood vessels](https://aihub.org/2023/12/06/interview-with-paula-feld

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