实验证明大模型算力每提升10倍，翻译效率和收入平均增16%。
Scaling Laws for Economic Productivity: Experimental Evidence in LLM-Assisted Translation
- 通过300名译员1800个任务实验，测试13个大模型算力与产出的关系。
- 算力增10倍，任务速度提12.3%，评分提升0.18个标准差，每分钟收入增16.1%。
- 低技能译员收益增幅是高技能者的4倍，适合关注人机协同的从业者。
本文基于大型语言模型（LLMs）训练算力与性能间的经验关系，推导出经济产出的“缩放定律”。在一项预注册的在线实验中，300名专业译员使用13种不同算力的LLM（或对照组）完成1800个任务。结果显示，模型算力提升十倍，任务完成速度提高12.3%，评分提升0.18个标准差，每分钟收入增加16.1%。低技能工作者的收益增长是高技能者的四倍。研究表明，未来十年持续模型扩展可使美国生产力至少提升6.9%。
原文摘要 · Abstract (English)
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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