Percy Liang @percyliang

Scaling laws depend on a metric that improves gradually with scale in order to extrapolate to a large target scale. We are interested in the accuracy metric, but you might get zero accuracy for small models. You can't extrapolate from a bunch of zeros. Key idea: introduce a *hint* level, and fit a joint scaling law Accuracy(scale, hint) = ... More hinting makes the problem easier in a controlled way, so accuracy is no longer zero, allowing for extrapolation! Learn more below. [引用 @suze_va]: Can we forecast benchmark accuracy from models that score zero? We (co-leads @LukeBailey181 @rckpudi) turn a benchmark into a series of easier hinted versions. Jointly modeling scale C and hint level H enables forecasting GSM8K acc (H=0) within 3 pts from 5600x less compute 🧵 https://t.co/fyLbvONHuL