David Smith

LLM as Critic as Artist

When it Rains, it Pours: Modeling Media Storms and the News Ecosystem Benjamin Litterer, David Jurgens, Dallas Card. Findings of the Association for Computational Linguistics: EMNLP 2023. 2023.

If you've made it this far, you might also want to check out Amber's earlier work on media storms: www.tandfonline.com/doi/abs/10.1..., or my student Ben Litterer's (@blitt.bsky.social) ACL paper on the same topic: aclanthology.org/2023.finding...

Catching Fire Appendix Cambridge Political Communication Element APPENDIX FOR   CATCHING FIRE IN THE NEWS

For additional details, including coding protocols, teaching resources, and side-by-side case comparisons, you can refer to the accompanying website: www.amber-boydstun.com/catching-fir...

We also discuss additional factors that can influence the course of a storm, such as journalistic gatekeeping, attention fatigue, political activism, and strategic communication online.
For a more in-depth summary, please take a look at Jill's thread here: bsky.app/profile/jill... or read the book!

The book is build around a series of paired case studies -- similar events, where one became a full-fledged media storm, and the other did not -- such as the Titan Submersible Implosion vs. the Messenia Migrant Boat Disaster, occurring just days apart in 2023.

The heart of this work uses the fire triangle model (heat, fuel, and oxygen) as a metaphor to characterize the necessary conditions for an event to become into a media storm -- those stories that are so pervasive in the news that they are practically inescapable.

Catching Fire in the News Cambridge Core - Politics: General Interest - Catching Fire in the News

I'm a little late in sharing this news, but thanks to the extraordinary efforts of Amber Boydstun, @jilllaufer.bsky.social, and @nlpnoah.bsky.social, our book on media storms, "Catching Fire in the News", is now published and available fully open-access from Cambridge! doi.org/10.1017/9781...

Memorization vs. generalization in deep learning: implicit biases, benign overfitting, and more Or: how I learned to stop worrying and love the memorization

What is the relationship between memorization and generalization in AI? Is there a fundamental tradeoff? In infinitefaculty.substack.com/p/memorizati... I’ve reviewed some of the evolving perspectives on memorization & generalization in machine learning, from classic perspectives through LLMs.

Wrote a slightly more detailed guide on how to do this with your own collections/materials: danielvanstrien.xyz/posts/2026/r...

New article! "Toward an Ontological Representation of Fictional Characters" by @antoine-bourgois.bsky.social, me, @oseminck.bsky.social & @tpoibeau.bsky.social

doi.org/10.1017/chr....

Nothing fancy here — only sweat & tears. 🧵

Symmetry in language statistics shapes the geometry of model representations Although learned representations underlie neural networks' success, their fundamental properties remain poorly understood. A striking example is the emergence of simple geometric structures in LLM rep...

In our new preprint, we explain how some salient features of representational geometry in language modeling originate from a single principle - translation symmetry in the statistics of data.

arxiv.org/abs/2602.150...

With Dhruva Karkada, Daniel Korchinski, Andres Nava, & Matthieu Wyart.

Privacy Ripple Effects from Adding or Removing Personal Information in Language Model Training YouTube video by Google TechTalks

I gave a talk at the Google Privacy in ML Seminar last summer on privacy & memorization: "Privacy Ripple Effects from Adding or Removing Personal Information in Language Model Training".

It's up on YouTube now if you're interested :)
youtu.be/IzIsHFCqXGo?...

We also show that we are far from done, specifically for a complicated language like Old French.

But we
(1) defined the issue,
(2) propose a first solution that enables pre-annotation of larger dataset and
(3) offer an alternative to less trustable models that go beyond ATR.

Pre Editorial Normalization - a Hugging Face Space by comma-project Latin and Old French normalization of CATMuS output

We release:

📚 4.66M silver training samples
🧪 1.8k gold evaluation set huggingface.co/datasets/com...
🤖 ByT5-based model → 6.7% CER huggingface.co/comma-projec...

Try it here 👇
huggingface.co/spaces/comma...

👉 We propose Pre-Editorial Normalization (PEN):

An intermediate layer between:
📝 graphemic ATR output
📖 fully edited text

Goal: preserve palaeographic fidelity + enable usability.
Keep two layer, ATR output and normalization, with aligned token to go back to the source.

Recent ATR progress—especially with palaeographic datasets like CATMuS—has improved access to medieval sources.

But:
❌ Raw outputs are hard to use
❌ Fully normalized models over-normalize & hallucinate

There’s a methodological gap.

If I give you the text
📚 omnium peccatorum quia ex quo dyaconus quando esset in futurum, stultus esset

Can you find the ATR error without the manuscript ?

Probably not.

ATR models that predict text and normalize in one go generate trustable text, but prevent detecting issues.

📄 New paper:
Pre-Editorial Normalization for Automatically Transcribed Medieval Manuscripts in Old French and Latin

Thibault Clérice, @rachelbawden.bsky.social , Anthony Glaise, Ariane Pinche, @dasmiq.bsky.social (2026) arxiv.org/abs/2602.13905

We introduce Pre-Editorial Normalization (PEN).

🧵⬇️

Excited to be co-organizing the #CHI2026 workshop on augmented reading interfaces 📚✨ Submissions are open for one more week! We want to know what you're working on!

our open model proving out specialized rag LMs over scientific literature has been published in nature ✌🏻

congrats to our lead @akariasai.bsky.social & team of students and Ai2 researchers/engineers

www.nature.com/articles/s41...

Cool postdoc job opportunity! A chance to work with some great English & comp sci scholars at Carnegie Mellon. Appreciate this ad stresses: chance to do interesting technical work; work on an interesting humanities problem; chance to publish both in humanities & comp sci venues. Looks great, apply!

The bias/variance tradeoff in 2026: Claude Sonnet wrote a program to solve the problem as described; Claude Opus figured out a shortcut from the example data that won't generalize.

[Job 📣] Are you curious about #AI applications in the #humanities? My Print and Probability research group (@print-and-prob.bsky.social) is hiring a postdoc! Come help us develop computational methods for identifying clandestine early modern printers!

cmu.wd5.myworkdayjobs.com/CMU/job/Pitt...

Debate me bro Statistical inference is rhetoric.

Statistical inference is a rhetoric of counts.

I had the absolute pleasure to visit @craicexeter.bsky.social, where I laid out an argument for how critical & computational scholars should lead the conversation on AI. We need to expand research on harms, interrogate corporate hype, and support people’s critical understanding these technologies

I couldn't figure out eloquent language to describe digital agents that assist with web navigation tasks, so I just wrote "click click" and if I keep this up maybe I will start referring to language generation as "word word"

Hi Honey, I’m Homo Neuricus Six Ways I'm using AI to Become More Human

A very random view into how some people* outside of tech think about and use chatbots. It's not coding, that's for sure, and some of it might sound ridiculous, but I think this kind of perspective and usage is way more common than we might assume.

*LA people (sorry, I love LA, but this is very LA)

Let's think step by step. If you could reconstruct the original page with high probability using a language model given the bag of words, you could:
1. demonstrate that bag of words models are useful, and
2. destroy the legal arguments people used to allow them to share bags of words.

The second new class I'm teaching is a very experimental graduate level seminar in CSE: "Building Small Language Models". I taught the grad level NLP class last semester (so fun!) but students wanted more—which of these new ideas work, and which work for SLMs? jurgens.people.si.umich.edu/CSE598-004/

For social scientists interested in LLMs for text classification/coding, the process here is potentially very helpful (even if you don't use the product itself).

Their core technique: Contradictory Example Training
Their training method: Binocular Labeling

More details in the linked post below.