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

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  • Ali Merali

Abstract

This paper derives 'scaling laws' -- empirical relationships between the amount of training compute used for a Large Language Model (LLM) and its performance -- for economic outcomes. In a preregistered experiment, 300 professional translators completed 1800 tasks with access to one of thirteen LLMs with differing model training compute sizes (or a control). Our results show that model scaling substantially raises productivity: for every 10x increase in model compute, translators completed tasks 12.3% quicker, received 0.18 s.d. higher grades, and earned 16.1% more per minute (including bonus payments). Further, the gains from model scaling are much higher for lower-skilled workers who gain a 4x larger improvement in task completion speed. These results imply further frontier model scaling -- which is currently estimated at 4x increase per year -- may have significant economic implications.

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  • Ali Merali, 2024. "Scaling Laws for Economic Productivity: Experimental Evidence in LLM-Assisted Translation," Papers 2409.02391, arXiv.org.
  • Handle: RePEc:arx:papers:2409.02391
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    References listed on IDEAS

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    1. Tyna Eloundou & Sam Manning & Pamela Mishkin & Daniel Rock, 2023. "GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models," Papers 2303.10130, arXiv.org, revised Aug 2023.
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