Tag: theory
Goldfish loss
2025-10-27 training theory text Apertus copyright It may be a problem for text models to generate exact quotes from training data. This paper looks at a simple modification to the training loss function, intended to prevent models from being able to generate exact quotes. The technique was adopted by the recent Apertus models in their pursuit of "compliance." Access: Free account (logged in)
Linear algebra intro
2025-10-06 basics theory math Introduction to basic concepts that are useful in reading papers: the meaning and purpose of mathematics; vectors; dot products; and matrices. Access: $ Basic
What's a Model?
2025-09-01 alignment basics theory text Gemma hallucination What do we actually mean when we talk about a "model"? Where do they come from? How much do they cost? What are prompts, loss functions, and fine-tuning? This extra-long introductory talk covers some of the basic concepts in the AI landscape, with a special focus on chatbots. Access: Public
Original diffusion: Adding noise to remove it
2025-08-04 theory image diffusion Some of the underlying theory for diffusion-type models, which have become popular for image generation. This paper is one of the original sources for the diffusion approach, not introduction of a specific model but the very general abstract concepts used in subsequent models. Access: Free account (logged in)
Embeddings from generative models
2025-08-01 theory applications attention Mistral For text generation you usually want a "decoder" model; for other text tasks you usually want an "encoder." Here we look at modifying a decoder model to change it into an encoder. Access: $ Basic
Features are not what you think
2025-08-01 theory security image Two interesting things about neural network image classifiers: one, the individual neurons don't seem to be special in terms of detecting meaningful features; and two, it's frighteningly easy to construct adversarial examples that will fool the classification. Access: $ Basic
Better (than) tokenization with BLTs
2025-08-01 theory text LLaMA tokenization Using "patches" of input bytes, instead of a fixed token list, allows better scalability and improves performance on some tasks that are hard for token-based LLMs. Access: $ Basic