Information Theory for Language Models: Jack Morris

Our last AI PhD grad student feature was Shunyu Yao, who happened to focus on Language Agents for his thesis and immediately went to work on them for OpenAI. Our pick this year is Jack Morris, who bucks the “hot” trends by -not- working on agents, benchmarks, or VS Code forks, but is rather known for his work on the information theoretic understanding of LLMs, starting from embedding models and latent space representations (always close to our heart). Jack is an unusual combination of doing underrated research but somehow still being to explain them well to a mass audience, so we felt this was a good opportunity to do a different kind of episode going through the greatest hits of a high profile AI PhD, and relate them to questions from AI Engineering. Papers and References made AI grad school: https://x.com/jxmnop/status/1933884519557353716 A new type of information theory: https://x.com/jxmnop/status/1904238408899101014 Embeddings Text Embeddings Reveal (Almost) As Much As Text: https://arxiv.org/abs/2310.06816 Contextual document embeddings https://arxiv.org/abs/2410.02525 Harnessing the Universal Geometry of Embeddings: https://arxiv.org/abs/2505.12540 Language models GPT-style language models memorize 3.6 bits per param: https://x.com/jxmnop/status/1929903028372459909 Approximating Language Model Training Data from Weights: https://arxiv.org/abs/2506.15553 https://x.com/jxmnop/status/1936044666371146076 LLM Inversion "There Are No New Ideas In AI.... Only New Datasets" https://x.com/jxmnop/status/1910087098570338756 https://blog.jxmo.io/p/there-are-no-new-ideas-in-ai-only misc reference: https://junyanz.github.io/CycleGAN/ — for others hiring AI PhDs, Jack also wanted to shout out his coauthor Zach Nussbaum, his coauthor on Nomic Embed: Training a Reproducible Long Context Text Embedder.