activity
20222025
most citedCompute Trends Across Three Eras of Machine Learning

312 citations · 445 across the 7 of their papers we have counts for

collaborators

7 papers

econ.GN2025

GATE: An Integrated Assessment Model for AI Automation

Ege Erdil, Andrei Potlogea, Tamay Besiroglu +6

Assessing the economic impacts of artificial intelligence requires integrating insights from both computer science and economics. We present the Growth and AI Transition Endogenous…

cs.AI2024★ 9 cited

FrontierMath: A Benchmark for Evaluating Advanced Mathematical Reasoning in AI

Elliot Glazer, Ege Erdil, Tamay Besiroglu +21

We introduce FrontierMath, a benchmark of hundreds of original, exceptionally challenging mathematics problems crafted and vetted by expert mathematicians. The questions cover most…

cs.CL2024★ 13 cited

Algorithmic progress in language models

Anson Ho, Tamay Besiroglu, Ege Erdil +6

We investigate the rate at which algorithms for pre-training language models have improved since the advent of deep learning. Using a dataset of over 200 language model evaluations…

cs.LG2023

Power Law Trends in Speedrunning and Machine Learning

Ege Erdil, Jaime Sevilla

We find that improvements in speedrunning world records follow a power law pattern. Using this observation, we answer an outstanding question from previous work: How do we improve…

cs.LG2022★ 82 cited

Will we run out of data? Limits of LLM scaling based on human-generated data

Pablo Villalobos, Anson Ho, Jaime Sevilla +3

We investigate the potential constraints on LLM scaling posed by the availability of public human-generated text data. We forecast the growing demand for training data based on cur…

cs.LG2022★ 29 cited

Machine Learning Model Sizes and the Parameter Gap

Pablo Villalobos, Jaime Sevilla, Tamay Besiroglu +3

We study trends in model size of notable machine learning systems over time using a curated dataset. From 1950 to 2018, model size in language models increased steadily by seven or…

cs.LG2022★ 312 cited

Compute Trends Across Three Eras of Machine Learning

Jaime Sevilla, Lennart Heim, Anson Ho +3

Compute, data, and algorithmic advances are the three fundamental factors that guide the progress of modern Machine Learning (ML). In this paper we study trends in the most readily…