LLeMpower: Understanding Disparities in the Control and Access of Large Language Models
This work addresses inequity in LLM access and control, highlighting concentration issues that affect global stakeholders, though it is incremental in its analysis of existing disparities.
The study analyzed the training and inference requirements of large language models (LLMs) to assess economic disparities in control and access, revealing that these technologies are monopolized by a surprisingly few entities, with qualitative ethical implications discussed.
Large Language Models (LLMs) are a powerful technology that augment human skill to create new opportunities, akin to the development of steam engines and the internet. However, LLMs come with a high cost. They require significant computing resources and energy to train and serve. Inequity in their control and access has led to concentration of ownership and power to a small collection of corporations. In our study, we collect training and inference requirements for various LLMs. We then analyze the economic strengths of nations and organizations in the context of developing and serving these models. Additionally, we also look at whether individuals around the world can access and use this emerging technology. We compare and contrast these groups to show that these technologies are monopolized by a surprisingly few entities. We conclude with a qualitative study on the ethical implications of our findings and discuss future directions towards equity in LLM access.