
Large language models, explained with a minimum of math and jargon
Date : 2023-07-27
Introduction
No one on Earth fully understands the inner workings of LLMs. Researchers are working to gain a better understanding, but this is a slow process that will take years—perhaps decades—to complete.
Still, there’s a lot that experts do understand about how these systems work. The goal of this article is to make a lot of this knowledge accessible to a broad audience. Timothy B Lee and Sean Trott aim to explain what’s known about the inner workings of these models without resorting to technical jargon or advanced math.
Read blog post here
Recently on :
Artificial Intelligence
WEB - 2025-11-13
Measuring political bias in Claude
Anthropic gives insights into their evaluation methods to measure political bias in models.
WEB - 2025-10-09
Defining and evaluating political bias in LLMs
OpenAI created a political bias evaluation that mirrors real-world usage to stress-test their models’ ability to remain objecti...
WEB - 2025-07-23
Preventing Woke AI In Federal Government
Citing concerns that ideological agendas like Diversity, Equity, and Inclusion (DEI) are compromising accuracy, this executive ...
WEB - 2025-07-10
America’s AI Action Plan
To win the global race for technological dominance, the US outlined a bold national strategy for unleashing innovation, buildin...
WEB - 2024-12-30
Fine-tune ModernBERT for text classification using synthetic data
David Berenstein explains how to finetune a ModernBERT model for text classification on a synthetic dataset generated from argi...