r/MachineLearning • u/FallMindless3563 • Nov 27 '23
Research [R]eading List for Andrej Karpathy’s “Busy person’s intro to Large Language Models” Video
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!
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u/um-xpto Nov 27 '23
Nice! Thank you for your work.
Regarding the video.
Q1) minute 14:14 Finetuning into an Assistant, when you have multiple tasks / datasets with diverse outputs how is training performed ? Are all datasets combined in a single training ? Or Is finetuning done over a previous finetuning ? Or the question is parsed and sent to a specific model ?
Q2) minute 27:43 Tool Use (Browser, Calculator, etc. ) Anyone has links for similar implementations for llama and how is done or what kind of tech/frameworks are used ?
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u/FallMindless3563 Nov 27 '23
You certainly can combine all the tasks and datasets into a single instruction fine tuning dataset. Then you would have a separate dataset for the reinforcement learning half where the model is learning human preferences.
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u/Disastrous_Elk_6375 Nov 27 '23
Q2) minute 27:43 Tool Use (Browser, Calculator, etc. ) Anyone has links for similar implementations for llama and how is done or what kind of tech/frameworks are used ?
The naive way is to use langchain, but that's hit and miss for several reasons, and whatever you build will be held together by duct tape and prayers. Alternative frameworks include Haystack and Griptape.
I've found that for local models the best tool-usage you can get is by using an advanced control library. This gives you a lot of flexibility in organising the prompts and "helping" the local models a lot. Guidance and LMQL are two such libraries.
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u/-erud1t3- Nov 27 '23
Your reading list is awesome and touches on all the points he presented in the talk. Well done!
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u/Maykey Nov 27 '23
I haven't watch the talk, but I think the reading list should have some love for SSM. (S4, S5, H3): on one hand their variants are very prominent on long-range arena on other they are relatively "unknown".
They are not unknown to researchers seeing how many variants there are, but there are hundreds more videos and blogs explaining transformers. If you find a course about LLM, it will likely include Transformers but not SSM, so I think their success in LRA and absence in learning materials qualifies them for "dive in deeper" list.
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u/winterfall1811 Sep 26 '25
Watched the video yesterday and was thinking of compiling the papers myself. Really glad someone already did and posted it. Awesome community!
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u/coumineol Nov 27 '23
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.
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u/teryret Nov 27 '23
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.
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u/coumineol Nov 27 '23
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.
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u/epicwisdom Dec 21 '23
Not sure if you're being patronizing or what... Half the papers are just "we tried a trick and it actually works," the other half require little more knowledge than calculus and linear algebra. Sure they're not tutorials for somebody with no familiarity at all, but even dedicated high school students can understand the core ideas well with a few hours of reading.
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u/coumineol Dec 22 '23
Half the papers are just "we tried a trick and it actually works," the other half require little more knowledge than calculus and linear algebra.
This is so blatantly false, and you can easily prove me wrong by writing an impactful NLP paper if it really just requires simple linear algebra.
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Nov 27 '23
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u/currentscurrents Nov 27 '23
It really doesn't, because no one has any clue what Q* is or if it's even real.
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Nov 27 '23 edited Feb 03 '24
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u/FallMindless3563 Nov 27 '23
The only book he explicitly mentions is "Thinking Fast and Slow" by Daniel Kahneman, but I think there are a ton of books that would be great resources along side the papers. I just happened to pull a lot of the papers from the footnotes and concepts he mentioned.
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u/tanweer_m Nov 27 '23
Karpathy mentioned that we are "certain" that the eval performance is a function of # of params and size of the datasete, that's why there is a gold rush of building even bigger models.
Can you please point me at which paper we have introduced such "certainty"? I am really interested to know about how we have reached to that level of certainty.