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| Format: | Preprint |
| Publié: |
2024
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| Accès en ligne: | https://arxiv.org/abs/2409.02391 |
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| _version_ | 1866912147563347968 |
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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 |