LLM Research (2 blogmarks)

This post is a survey of several papers and techniques from 2012 to 2017 that were influential in the development of the modern Transformer Architecture. Which in turn is what is behind the rapid advancement in Large Language Models.

I like surveys like this because they give a high-level overview that can contextualize and point at both things I've already read about like Word2Vec as well as things that are new to me like Adam.

The people at latent.space have curated a list of ~50 papers across ten areas for AI engineers looking to dig into relevant research in the AI and LLM space in 2025.

Here we curate “required reads” for the AI engineer. Our design goals are:

• pick ~50 papers (~one1 a week for a year), optional extras. Arbitrary constraint.
• tell you why this paper matters instead of just name drop without helpful context
• be very practical for the AI Engineer; no time wasted on Attention is All You Need, bc 1) everyone else already starts there, 2) most won’t really need it at work

Funny to see that they completely side-step "Attention is All You Need". As foundational as that paper is, they consider it old news and not practical for engineers in 2025.

There is a repo LLM Practical Guide full of papers and an "LLM Tree" diagram that may have been a partial source of inspiration for the latent.space paper clubs. Unfortunately, this repo hasn't been updated since 2023. That said, there are still a lot of good resources in there.

This "LLM Tree" diagram is excellent showing the different branches of LLM tooling over time.