Short Little Difficult Books | Discussion

Short Little Difficult Books

Novels that challenge with style, story, or form that you can read in a day.

countercraft.substack.com

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...

Why AI writing is mid

How the current way of training language models destroys any voice (and hope of good writing).

interconnects.ai

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. 1/n

Torching the Modern-Day Library of Alexandria

“Somewhere at Google there is a database containing 25 million books and nobody is allowed to read them.”

theatlantic.com

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 👇

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

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 🧵 (1/7)

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. 🧵

Ai2@ai2.bsky.social · 11mo ago

🚀 Introducing Fluid Benchmarking—an adaptive way to evaluate LLMs. Inspired by psychometrics, it tailors which questions to ask based on each model’s capability, making evals more efficient & reliable. 🧵

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.

Shaily@shaily99.bsky.social · last yr.

🖋️ Curious how writing differs across (research) cultures? 🚩 Tired of “cultural” evals that don't consult people? We engaged with interdisciplinary researchers to identify & measure ✨cultural norms✨in scientific writing, and show that❗LLMs flatten them❗ 📜 arxiv.org/abs/2506.00784 [1/11]

🤔 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 🥳

@nhminhle.bsky.social · last yr.

LLMs memorize novels 📚 in English. But what about existing translations? Or translations into new languages? Our 🦉OWL dataset (31K/10 languages) shows GPT4o recognizes books: 92% English 83% official translations 69% unseen translations 75% as audio (EN)

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! 🧵👇

📚 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!

Learning to Reason for Long-Form Story Generation

Generating high-quality stories spanning thousands of tokens requires competency across a variety of skills, from tracking plot and character arcs to keeping a consistent and engaging style. Due to…

buff.ly

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.

Yixiao Song@yixiaosong.bsky.social · last yr.

Introducing 🐻 BEARCUBS 🐻, a “small but mighty” dataset of 111 QA pairs designed to assess computer-using web agents in multimodal interactions on the live web! ✅ Humans achieve 85% accuracy ❌ OpenAI Operator: 24% ❌ Anthropic Computer Use: 14% ❌ Convergence AI Proxy: 13%

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 🧵👇

Excited to share our preprint "Provocations from the Humanities for Generative AI Research” We're open to feedback—read & share thoughts! @laurenfklein.bsky.social @mmvty.bsky.social @docdre.distributedblackness.net @mariaa.bsky.social @jmjafrx.bsky.social @nolauren.bsky.social @dmimno.bsky.social

🚨 New Position Paper 🚨 Multiple choice evals for LLMs are simple and popular, but we know they are awful 😬 We complain they're full of errors, saturated, and test nothing meaningful, so why do we still use them? 🫠 Here's why MCQA evals are broken, and how to fix them 🧵

⚠️Current methods for generating instruction-following data fall short for long-range reasoning tasks like narrative claim verification. We present CLIPPER ✂️, a compression-based pipeline that produces grounded instructions for ~$0.5 each, 34x cheaper than human annotations.

🤖🍲 What can LLMs do for sustainable food? 🤖🍲 We collaborated with domain experts (food scientists and chefs) to define a typology of food design and prediction tasks. LLMs can assist in food and menu development, saving food scientists' time and reducing emissions! URL: bit.ly/3ERJbUV

People often claim they know when ChatGPT wrote something, but are they as accurate as they think? Turns out that while general population is unreliable, those who frequently use ChatGPT for writing tasks can spot even "humanized" AI-generated text with near-perfect accuracy 🎯

Excited to share that today our paper recommender platform www.scholar-inbox.com has reached 20k users! We hope to reach 100k by the end of the year.. Lots of new features are being worked on currently and rolled out soon.