Craig Messner

New to me as well, will be using this in class this semester!

From my own local extrapolations, it feels like methods tied to classic quant dh/cultural analytics are getting broader play, and that "humanities machine learning" (which extends ML fields like interpretability, data-efficient training, evaluation and etc.) is emerging underneath

The real question is: who is winning?

Any leads on research that examines what disjoint exists between human-recoverable and SAE recoverable features out there? (Perhaps after: arxiv.org/html/2506.15..., which features are "naturally" distinguishable by humans but represented densely by SAEs, which end up in "noisy" dense reps.)

Anyone have on instruction tuning for models trained solely on historical data? Turns out texts from 1750 have very few "reddit-like" constructs.

Interestingly this reads to me a lot like a description of how close reading works in practice, especially post new-historicism -- "why this word here, knowing what we know about its contemporaneous use"

More importantly, its frustrations will hopefully serve as useful critical lessons. I also hereby claim the use of the name "Poetaster" for any further such systems!

GitHub - messner1/poetaster: Demo of a RAG-based educational game on an EmbeddingGemma/quantized Llama backbone Demo of a RAG-based educational game on an EmbeddingGemma/quantized Llama backbone - messner1/poetaster

I teach a machine learning class for students in traditionally less computational fields (cdh.jhu.edu/teaching/4/). Recent students had questions about RAG, so I used EmbeddingGemma's release as an excuse to put together an example in the form of a poetry criticism game

github.com/messner1/poe...