Scaling Laws for Economic Productivity: Experimental Evidence in LLM-Assisted Translation

2024/09/04 by Ali Merali, Ali merali, Merali, Ali · 1 voice · 1 citation
Economics, Econometrics and Finance · Social Sciences · #Italy: Economic History and Contemporary Issues #Diverse Scientific and Economic Studies #Media Influence and Politics

paper · pdf · doi:10.48550/arxiv.2409.02391

Abstract

This paper derives "scaling laws"--empirical relationships between the training compute of Large Language Models (LLMs) and their performance--for economic outcomes. In a preregistered online experiment, 300 professional translators completed 1,800 tasks using one of 13 LLMs (or a control). A tenfold increase in model compute improved task completion speed by 12.3%, grades by 0.18 standard deviations, and earnings per minute by 16.1%. Gains were four times larger for lower-skilled workers. These findings suggest continued model scaling could boost U.S. productivity by at least 6.9% over the next decade.

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• Will Scaling Improve Social Simulation with LLMs?

Discussions

• AI ability is famously driven by scale but this scaling law may extend to user productivity Testing on professional translators “for every 10x increase in model compute, translators completed tasks 1 [bsky, 9 points, 0 comments]

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