I understand that it’s about LLMs and that it is not comprehensive — also that the site author has (perhaps questionably) embedded some of their own work in the list. That said, it does otherwise appear to be a list of influential papers representing a current major thrust.
Point taken on the "gold rush". My background is CS Theory so the incorporation of combinatorial methods feels right at home. Along these lines, are you aware of the use of any work incorporating (combinatorial) logic verification into generative language models? The end goal would be improved argument synthesis (e.g. mathematical proofs, say)
I'm an outsider interested in learning the landscape so my intent is to leave the question open-ended, but I'm broadly interested in architectural things like layer-design, attention mechanisms, regularization, model compression, as well as bigger picture considerations like interpretability, explainability, and fairness.
alfredr OP t1_jcz3keg wrote
Reply to comment by lmericle in [R] What are the current must-read papers representing the state of the art in machine learning research? by alfredr
I understand that it’s about LLMs and that it is not comprehensive — also that the site author has (perhaps questionably) embedded some of their own work in the list. That said, it does otherwise appear to be a list of influential papers representing a current major thrust.
I did not downvote you, btw