Short Little Difficult Books | Discussion
18.11.2025 15:20
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Why AI writing is mid
How the current way of training language models destroys any voice (and hope of good writing).
www.interconnects.ai/p/why-ai-wri...
I curated some readings for class on "data tensions" and the list felt worth sharing. Come on a tour of datasets, books, the web, and AI with me...
We'll start with this piece on the Google Books project: the hopes, dreams, disasters, and aftermath of building a public library on the internet.
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Excited to share our new paper, "DataRater: Meta-Learned Dataset Curation"!
We explore a fundamental question: How can we *automatically* learn which data is most valuable for training foundation models?
Paper: arxiv.org/pdf/2505.17895 to appear at @neuripsconf.bsky.social
Thread 👇
Screenshot that reads: Introducing the Anthology for Computers and the Humanities Taylor Arnold, Maria Antoniak, Miguel Escobar Varela, Marie Puren, Mila Oiva , Amanda Regan, Lauren Tilton, and Melanie Walsh 1 Data Science and Statistics, University of Richmond, U.S.A. 2 Computer Science, University of Colorado Boulder, U.S.A. 3 Faculty of Arts and Social Sciences, National University of Singapore 4 Laboratoire de Recherche de l'EPITA, Paris, France 5 History and Archaeology, University of Turku, Finland 6 History and Geography, Clemson University, U.S.A. 7 Rhetoric and Communication Studies, University of Richmond, U.S.A. 8 Information School, University of Washington, U.S.A. Permanent Link: https://doi.org/10.63744/HHsQG7hNWyxG Published: 25 September 2025
As DH grows, it’s increasingly important to publish conference papers, but there hasn’t been a clear venue for that.
So I’m thrilled to share this new home for DH proceedings, which will include CHR papers & more.
Thanks to @taylor-arnold.bsky.social for leading this effort!
bit.ly/ach-anthology
A diagram illustrating pointwise scoring with a large language model (LLM). At the top is a text box containing instructions: 'You will see the text of a political advertisement about a candidate. Rate it on a scale ranging from 1 to 9, where 1 indicates a positive view of the candidate and 9 indicates a negative view of the candidate.' Below this is a green text box containing an example ad text: 'Joe Biden is going to eat your grandchildren for dinner.' An arrow points down from this text to an illustration of a computer with 'LLM' displayed on its monitor. Finally, an arrow points from the computer down to the number '9' in large teal text, representing the LLM's scoring output. This diagram demonstrates how an LLM directly assigns a numerical score to text based on given criteria
LLMs are often used for text annotation, especially in social science. In some cases, this involves placing text items on a scale: eg, 1 for liberal and 9 for conservative
There are a few ways to accomplish this task. Which work best? Our new EMNLP paper has some answers🧵
arxiv.org/pdf/2507.00828
AI is already at work in American newsrooms.
We examine 186k articles published this summer and find that ~9% are either fully or partially AI-generated, usually without readers having any idea.
Here's what we learned about how AI is influencing local and national journalism:
"AI slop" seems to be everywhere, but what exactly makes text feel like "slop"?
In our new work (w/ @tuhinchakr.bsky.social, Diego Garcia-Olano, @byron.bsky.social ) we provide a systematic attempt at measuring AI "slop" in text!
arxiv.org/abs/2509.19163
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Keynote at #COLM2025: Nicholas Carlini from Anthropic
"Are language models worth it?"
Explains that the prior decade of his work on adversarial images, while it taught us a lot, isn't very applied; it's unlikely anyone is actually altering images of cats in scary ways.
📢 New #COLM2025 paper 📢
Standard benchmarks give every LLM the same questions. This is like testing 5th graders and college seniors with *one* exam! 🥴
Meet Fluid Benchmarking, a capability-adaptive eval method delivering lower variance, higher validity, and reduced cost.
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What are your favorite recent papers on using LMs for annotation (especially in a loop with human annotators), synthetic data for task-specific prediction, active learning, and similar?
Looking for practical methods for settings where human annotations are costly.
A few examples in thread ↴
I see this work as our answer to the "cultural alignment" and "cultural benchmarking" trends in NLP research. Instead of making decisions for people, we consider "culture" in a specific setting with specific people for a specific task, and we ask people directly about their cultural adaptations.
We release code to facilitate future research on fine-grained detection of mixed-origin texts and human-AI cowriting.
Github: github.com/chtmp223/Fra...
Paper: arxiv.org/abs/2505.18128
Work done with @jennajrussell, @dzungvietpham, and @MohitIyyer!
Room for improvement:
🔧 Frankentexts struggle with smooth narrative transitions and grammar, as noted by human annotators.
🔩 Non-fiction versions are coherent and faithful but tend to be overly anecdotal and lack factual accuracy.
Takeaway 2: Our controllable generation process provides a sandbox for human-AI co-writing research, with adjustable proportion, length, and diversity of human excerpts.
👫 Models can follow copy constraints, which is a proxy for % of human writing in co-authored texts.
Takeaway 1: Frankentexts don’t fit into the "AI vs. human" binary.
📉 Binary detectors misclassify them as human-written
👨👩👧 Humans can detect AI involvement more often
🔍 Mixed-authorship tools (Pangram) help, but still catch only 59%
We need better tools for this gray zone.
Automatic evaluation on 100 Frankentexts using LLM judges, text detectors, and a ROUGE-L-based metric shows that:
💪 Gemini-2.5-Pro, Claude-3.5-Sonnet, and R1 can generate Frankentexts that are up to 90% relevant, 70% coherent, and 75% traceable to the original human writings.
Frankentext generation presents an instruction-following task that challenges the limits of controllable generation, requiring each model to:
1️⃣ Produce a draft by selecting & combining human-written passages.
2️⃣ Iteratively revise the draft while maintaining a copy ratio.
🤔 What if you gave an LLM thousands of random human-written paragraphs and told it to write something new -- while copying 90% of its output from those texts?
🧟 You get what we call a Frankentext!
💡 Frankentexts are surprisingly coherent and tough for AI detectors to flag.
We find that LLMs (e.g. GPT-4o, LLaMA-3.1) consistently recall book content across languages, even for texts without official translation in pre-training data!
Great work led by undergrads at UMass NLP 🥳
A visualization of the generator-validator gap, where the LM likelihoods of for the generator and discriminator forms of questions are poorly correlated.
Aligning the validator and generator rankings can fix it!
One of the ways that LLMs can be inconsistent is the "generator-validator gap," where LLMs deem their own answers incorrect.
🎯 We demonstrate that ranking-based discriminator training can significantly reduce this gap, and improvements on one task often generalize to others!
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📚 Check out the newest JCA article by Li Lucy (@lucy3.bsky.social), Camilla Griffiths, Claire Ying, JJ Kim-Ebio, Sabrina Baur, Sarah Levine, Jennifer L. Eberhardt, David Bamman (@dbamman.bsky.social), and Dorottya Demszky. culturalanalytics.org/article/1316...
A very cool paper shows that you can use the RL loss to improve story generation by some clever setups on training on known texts (e.g. ground predictions versus a next chapter you know). RL starting to generalize already!
Leaderboard showing performance of language models on claim verification task over book-length input. o1-preview is the best model with 67.36% accuracy followed by Gemini 2.5 Pro with 64.17% accuracy.
We have updated #nocha, a leaderboard for reasoning over long-context narratives 📖, with some new models including #Gemini 2.5 Pro which shows massive improvements over the previous version! Congrats to #Gemini team 🪄 🧙 Check 🔗 novelchallenge.github.io for details :)
New paper from our team @GoogleDeepMind!
🚨 We've put LLMs to the test as writing co-pilots – how good are they really at helping us write? LLMs are increasingly used for open-ended tasks like writing assistance, but how do we assess their effectiveness? 🤔
arxiv.org/pdf/2503.19711
Our lab had a #dogathon 🐕 yesterday where we analyzed NYC Open Data on dog licenses. We learned a lot of dog facts, which I’ll share in this thread 🧵
1) Geospatial trends: Cavalier King Charles Spaniels are common in Manhattan; the opposite is true for Yorkshire Terriers.
The high effort solution is to use an LLM to make a browser extension which tracks your academic reading and logs every paper you interact with to github, which builds and publishes a webapp to expose the data.
Which, clearly only a crazy weirdo would do.
dmarx.github.io/papers-feed/
💡New preprint & Python package: We use sparse autoencoders to generate hypotheses from large text datasets.
Our method, HypotheSAEs, produces interpretable text features that predict a target variable, e.g. features in news headlines that predict engagement. 🧵1/
Ask OpenAI Operator for bus routes from your home in Vietnam to a university and it likely fails because it refuses to use Google Maps! Our new BEARCUBS 🐻 benchmark shows CU agents still struggle with seemingly straightforward multimodal questions.
Is the needle-in-a-haystack test still meaningful given the giant green heatmaps in modern LLM papers?
We create ONERULER 💍, a multilingual long-context benchmark that allows for nonexistent needles. Turns out NIAH isn't so easy after all!
Our analysis across 26 languages 🧵👇
