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

Ali Merali
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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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经济生产力的规模法则:法学硕士辅助翻译的实验证据
本文推导出了 "缩放定律"--大语言模型(LLM)所使用的训练计算量与模型性能之间的经验关系--经济效益。在一项预先登记的实验中,300 名专业翻译人员完成了 1800 项任务,并使用了 13 个具有不同模型训练计算量的大型语言模型之一(或一个对照组)。我们的结果表明,模型扩展大大提高了生产率:模型计算量每增加 10 倍,译员完成任务的速度就会加快 12.3%,获得的成绩就会提高 0.18 个标准差,每分钟的收入就会增加 16.1%(包括奖金)。此外,低技能工人从模型扩展中获得的收益更高,他们的任务完成速度提高了 4 倍。这些结果意味着进一步的前沿模型扩展(目前估计每年增加 4 倍)可能会产生重大的经济影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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