Are you Slop? Analyze your 'slop score' - a measure of how generic or unique your text appears to language models.
Andrej Karpathy on Twitter / X
We'll be spending a lot more time trying to understand the outputs of language models. A few thoughts, tips & tricks:Writing. Something I've had success with: Ask your LLM to explain something in ASD-STE100, it's a controlled language specification originally developed for… pic.twitter.com/uMFWTcBCsl— Andrej Karpathy (@karpathy) October 2, 2026
Stanford CS336 Language Modeling from Scratch I 2025
How linguistics learned to stop worrying and love the language models
Language models (LMs) can produce fluent, grammatical text. Nonetheless, some maintain that language models don’t really learn language and also, even if they did, that would not be informative for the study of human learning and processing. On the other side, there have been claims that the success of LMs obviates the need for studying linguistic theory and structure. We argue that both extremes are wrong. LMs can contribute to fundamental questions about linguistic structure, language processing, and learning. They force us to rethink arguments and ways of thinking that have been foundational in linguistics. While they do not replace linguistic structure and theory, they serve as model systems and working proofs of concept for gradient, usage-based approaches to language. We offer an optimistic take on the relationship between language models and linguistics.
Scaling Laws Across Model Architectures: A Comparative Analysis of...
The scaling of large language models (LLMs) is a critical research area for the efficiency and effectiveness of model training and deployment. Our work investigates the transferability and...
Self-Instruct: Aligning Language Models with Self-Generated Instructions
Large "instruction-tuned" language models (i.e., finetuned to respond to instructions) have demonstrated a remarkable ability to generalize zero-shot to new tasks. Nevertheless, they depend...
Wikipedia:Signs of AI writing
This is a list of writing and formatting conventions typical of AI chatbots such as ChatGPT, with real examples taken from Wikipedia articles and drafts. It is a field guide to help detect undisclosed AI-generated content on Wikipedia: while some of the signs may be broadly applicable, some may not apply in a non-Wikipedia context.[a] Not all text featuring these indicators is AI-generated, as the large language models that power AI chatbots are trained on human writing, including Wikipedia. Many elements of AI writing can be found in editorials, blogs, or fan fiction.
PoliSim@CHI 2026
Large Language Models are rapidly evolving from text generators into reasoning systems that can act as autonomous agents. When placed in social contexts, these agents display emergent behaviors such as forming coalitions, spreading information, and making collective decisions.
LLM cliché highlighter
Paste text below — or load it from a URL — and it highlights sentences that match known LLM clichés, including the tells catalogued in Wikipedia’s Signs of AI writing guide. Chain patterns like “no X, no Y” get a badge counting their items; tap or hover a highlight to see which cliché it hit.
AI Detector — Verified AI Content Checker | Pangram
Detect AI-generated text in essays, articles & documents. Identifies ChatGPT, Claude, Gemini & more with 99.98% accuracy. Third-party verified results.
OUTLIER · Daily Global Word Game Scored on Rarity
The daily global word game where rare words win. Same grid worldwide. Can you find what everyone else misses?
In the Battle for Attention
Slop is gaining ground. Low-quality content, including AI-generated trash, is advancing on all fronts for attention.
Machine Learning Glossary | Google for Developers
This glossary defines a wide range of machine learning terms, including those specific to TensorFlow and large language models.
AI Index | Stanford HAI
The mission of the AI Index is to provide unbiased, rigorously vetted, and globally sourced data for policymakers, researchers, journalists, executives, and the general public to develop a deeper understanding of the complex field of AI. To achieve this, we track, collate, distill, and visualize dat
Welcome | Yale Grammatical Diversity Project: English in North America
This Project explores syntactic diversity found in varieties of English spoken in North America. By documenting the subtle, but systematic, differences in the syntax of English varieties, it provides a crucial source of data for the development of theories of human linguistic knowledge.
Can gzip be a language model?
A while back I wrote about language modeling without neural networks, where I generated Shakespeare with an unbounded n-gram model: no weights, no training, …