Scaling Laws for Economic Productivity: Experimental Evidence in LLM-Assisted Translation
<|reference_start|>Scaling Laws for Economic Productivity: Experimental Evidence in LLM-Assisted Translation: 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.<|reference_end|>
@article{merali2024scaling,
    title={Scaling Laws for Economic Productivity: Experimental Evidence in
  LLM-Assisted Translation},
    author={Ali Merali},
    journal={arXiv preprint arXiv:2409.02391},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.02391},
    primaryClass={econ.GN cs.AI q-fin.EC}
}