I loved Andrej’s talk about in his “Busy person’s intro to Large Language Models” video, so I decided to create a reading list to dive in deeper to a lot of the topics. I feel like he did a great job of describing the state of the art for anyone from an ML Researcher to any engineer who is interested in learning more.
The full talk can be found here: https://youtu.be/zjkBMFhNj_g?si=fPvPyOVmV-FCTFEx
Here’s the reading list: https://blog.oxen.ai/reading-list-for-andrej-karpathys-intro-to-large-language-models-video/
Let me know if you have any other papers you would add!
Thanks but here’s the problem with this list: most of the papers mentioned are on a very high technical level, and people who would be able to understand them are probably people who have already read them. Note that Andrej was careful to keep the material at a certain level because he addresses those who want to go one step further than talking to ChatGPT, without necessarily understanding all the underlying theory.
Right, that’s why OP prefaced with “to dive deeper into a lot of the topics”. If folks aren’t at a point where diving deeper makes sense, it’s not a list for them. There are plenty of resources for any given level of understanding, obviously no list is going to be appropriate for every member of a diverse community.
Not to start an argument here but I can’t imagine anybody with any level of understanding who should start diving deeper by reading the “Attention is All You Need” paper. Yes, this is a diverse community, but when you try to address everybody’s needs, you usually end up with addressing nobody’s needs.
Since “Attention is All You Need” is fairly high on my reading list for understanding the details of transformer architecture, what do you recommend instead?
https://arxiv.org/abs/2106.04554
If you’re trying to learn more about language models don’t bother with anything written before 2020. That’s basically the Stone Age.
Thank you!
Just me, but I think of busy coworkers with great background in math/stats and ‘classic’ ML who would ramp up quickly from a list like this. When I onboarded chemists (PhDs) to my ML team at a drug startup, I would send them a similarly dense reading list. With their strong background in physics, it would take them two weeks flat to understand the necessary theory and jargon to be productive (in our niche field).
Didn’t mean to say those papers are completely useless, but even for those with a strong Math/ML background I would advise starting with recent survey papers. Reading “Attention is All You Need” is kind of like reading the General Relativity papers of Einstein - cool as a historical curiosity, but not ideal for optimizing expertise acquisition.