Decoding Generative AI Research Papers with Chatbots

This is the first introductory article in a series of blog posts. In these posts, we will break down some of the world’s most important papers on Generative AI, making them easier to understand.


If you’re interested in learning about AI, you know that one of the best ways to do so is to ask AI to explain itself. In fact, Barron’s recently published an article titled “We Asked Three Chatbots to Explain Generative AI. Here’s the Best Answer.”

While you can certainly ask AI simple questions like “Explain AI in layman’s terms,” and chatbots will do just that, the problem is that they often oversimplify things and leave out important concepts. That’s when we had an idea: “What if we used chatbots like ChatGPT, Claude, Mistral, Gemini, and Grok to help us decode the most prominent research papers on Generative AI?”

The goal isn’t just to “translate” expert jargon into layman’s terms, but to create a compelling learning journey. That’s how this article series was born. In this post, we’ll lay out our plan for using chatbots to help us understand the most important research papers on Generative AI.


Before we dive in, we need to ask ourselves a few important questions:

  1. How do we know which papers are important? The obvious answer is to ask AI. But to get the most accurate information, we need to use all the prompt engineering tricks we know to get as much information from our chatbots as possible. The saying “garbage in, garbage out” applies to chatbots like ChatGPT and Claude just as much as it does to any other knowledge system.
  2. How can we fact-check what we learn? We know that chatbots can sometimes “hallucinate” or provide inaccurate information. To overcome this limitation, we need to apply proper prompting techniques for fact-checking and examining the AI’s reasoning. However, at some point, we need to break the cycle of engaging with AI and talk to a human expert who can challenge us and confirm whether we’ve done a good job of using AI to understand AI.
  3. What learning methods do we use? We’re big fans of Ultralearning. According to ChatGPT, “Ultralearning is an intense approach to learning that aims to enable individuals to acquire new skills and knowledge as quickly and efficiently as possible. This self-directed learning strategy involves deep concentration, rapid feedback, and the application of sophisticated learning techniques. It demands a high degree of effort and focus, as it typically involves a commitment to mastering a specific skill or body of knowledge in the shortest amount of time. The concept of Ultralearning was popularized by Scott H. Young in his book ‘Ultralearning: Master Hard Skills, Outsmart the Competition, and Accelerate Your Career’. According to Young, this aggressive learning strategy can be employed by anyone who is motivated and willing to adopt a different approach to learning.”


Explaining AI as a layman to fellow laymen can be a challenge, but there is a well-known figure who advocates for this approach: Richard Feynman. If you’re not familiar with Feynman and his learning technique, here’s what ChatGPT has to say:

“Richard Feynman was an American theoretical physicist known for his work in the path integral formulation of quantum mechanics, the theory of quantum electrodynamics, and the physics of the superfluidity of supercooled liquid helium. He received the Nobel Prize in Physics in 1965.

The Feynman Technique is a learning strategy that Feynman reportedly used to understand complex concepts. It consists of four steps:

  1. Choose a topic you want to understand and start studying it. Write down everything you know about the topic on a notebook page, and add to that page every time you learn something new about it.
  2. Pretend to teach your topic to a classroom. Make sure you’re able to explain the topic in simple terms.
  3. Go back to the books when you get stuck. The gaps in your knowledge should become apparent. Revisit problem areas until you can explain the topic fully.
  4. Simplify and use analogies. Repeat the process while simplifying your language and connecting facts with analogies to help strengthen your understanding.

We believe that by following the Feynman Technique, we can make complex AI concepts accessible to our readers: “If you want to learn something, teach it.” We’ll be using this approach throughout our article series.


Where can we find high-quality research papers on Generative AI? Our assumption is that both companies that build the technology and universities are good sources. Let’s ask ChatGPT. Here’s the response:


  • Stanford University: Stanford’s Artificial Intelligence Lab has produced significant work in various branches of AI, including Generative AI.
  • Massachusetts Institute of Technology MIT: The Computer Science and Artificial Intelligence Laboratory CSAIL at MIT is known for pioneering research in AI.
  • University of Montreal: The Montreal Institute for Learning Algorithms MILA at the University of Montreal has been particularly influential in the field of deep learning and Generative AI.


  • Google: Google’s research teams, particularly Google Brain and DeepMind, have published extensively on deep learning and Generative AI.
  • OpenAI: As an AI research lab, OpenAI has made numerous contributions to the field of Generative AI, including the development of advanced language models like GPT-3.
  • Facebook AI Research FAIR: FAIR is known for conducting and publishing cutting-edge research in various areas of AI, including Generative AI.


We assume that this list is not exhaustive, and companies such as Apple for example contribute as well. “By the way, how about Chinese companies?” we thought. However, the list above is a good starting point. Let’s pick Google Research, OpenAI, and Facebook and see what results we get from ChatGPT:

“Google Research:

  • “Transformers: Attention Is All You Need”: This is one of the most influential papers in the natural language processing field, introducing the concept of transformers, which have since formed the basis of many artificial intelligence models.
  • “Large Scale Distributed Deep Networks”: This paper presents Google’s work on deploying large-scale neural networks across distributed infrastructure.
  • “EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks”: This paper introduces a new scaling method for neural networks, which has improved the performance of many image processing models.


  • “Language Models are Few-Shot Learners”: This paper presents GPT-3, one of the most powerful language models currently available, capable of generating impressively human-like text.
  • “Dactyl: Solving a Rubik’s Cube with a Robot Hand”: OpenAI demonstrates a groundbreaking use of reinforcement learning to train a robotic hand to solve a Rubik’s cube.

Facebook AI:

  • “DeepFace: Closing the Gap to Human-Level Performance in Face Verification”: This paper presents DeepFace, a deep learning model for face recognition which achieved near-human level performance.
  • “Exploring the Limits of Weakly Supervised Pretraining”: Facebook AI discusses how they trained a model on 3.5 billion publicly available images, significantly improving the state-of-the-art for various image recognition benchmarks.”

We’ll start with the first paper on the list: “Attention Is All You Need” in our next blog post. Stay tuned!

This article was a collaborative effort between us and AI. We provided the foundation, while AI handled the research, polishing, editing, and improving our writing style. We found that Mistral performed the best in comparison to ChatGPT and Gemini. Mistral was able to maintain the original tone of voice, while ChatGPT and Gemini made it sound like the article was written by a chatbot.

Rafael Knuth

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