A trained and tested LLM, when presented with a new text prompt, will generate the most likely next word, append it to the prompt, generate another next word, and continue in this manner, producing a seemingly coherent reply. Nothing in the training process suggests that bigger LLMs, built using more parameters and training data, should also improve at tasks that require reasoning to answer.
But they do. Big enough LLMs demonstrate abilities — from solving elementary math problems to answering questions about the goings-on in others’ minds — that smaller models don’t have, even though they are all trained in similar ways.
“Where did that [ability] emerge from?” Arora wondered. “And can that emerge from just next-word prediction?” —Quanta Magazine
MLA In-text citations: Writing that got you through high school won’t do in college.
Joe Biden gives the media a desperately needed lesson about Donald Trump
Duke stops assigning numeric values to essays, test scores
No focus, no fights, and a bad back – 16 ways technology has ruined my life
Nor the Battle to the Strong #StarTrek #DS9 Rewatch (Season 5, Episode 4) Jake Sisko, cub ...
I just tried Open Shot Video Editor, a free open source tool