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Auteur principal: Merali, Ali
Format: Preprint
Publié: 2024
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Accès en ligne:https://arxiv.org/abs/2409.02391
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author Merali, Ali
author_facet Merali, Ali
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2409_02391
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Scaling Laws for Economic Productivity: Experimental Evidence in LLM-Assisted Translation
Merali, Ali
General Economics
Economics
Artificial Intelligence
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.
title Scaling Laws for Economic Productivity: Experimental Evidence in LLM-Assisted Translation
topic General Economics
Economics
Artificial Intelligence
url https://arxiv.org/abs/2409.02391