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

• URL: http://arxiv.org/abs/2409.02391v2
• Date: Sat, 07 Dec 2024 08:56:53 GMT
• Title: Scaling Laws for Economic Productivity: Experimental Evidence in LLM-Assisted Translation
• Abstract summary: 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%.<n>These findings suggest continued model scaling could boost U.S. productivity by at least 6.9% over the next decade.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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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