video1.0<iframe src="https://www.loom.com/embed/ccb9593dbd354fd9904b18972211d6a8" frameborder="0" width="1708" height="1281" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>12811708Loomhttps://www.loom.com12811708https://cdn.loom.com/sessions/thumbnails/ccb9593dbd354fd9904b18972211d6a8-8c677349f8255517.gif97.381In this video, I explore whether language models can perform everyday math like humans do, particularly in terms of approximation. We conducted tests with 1,000 multiple-choice problems involving basket sums, discounts, taxes, unit prices, and tips. Our findings reveal that models tend to compute exact answers before rounding, which contrasts with human estimation methods. I encourage you to check out our code and data if you're interested in building models that can effectively choose when to approximate.