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

Ali Merali

Papers from arXiv.org

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.

Date: 2024-09, Revised 2024-12
New Economics Papers: this item is included in nep-ain, nep-eff, nep-exp and nep-ipr
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