AIERAAll topics
LLMs
Tokenizers, attention, training and evaluating large language models.
Attention, step by step: how a transformer decides what to look at
Queries, keys and values sound abstract until you follow a few tokens through the math by hand.
Tokenizers are where your model's quirks start
Byte-pair encoding explains why language models struggle to count letters and do arithmetic on long numbers.
Evaluating LLM outputs without fooling yourself
Contaminated benchmarks, position bias in model graders, and how to build an eval set you can trust.