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arxiv: 2404.09356 · v1 · pith:FI6RRC5Mnew · submitted 2024-04-14 · 💻 cs.CY · cs.AI· cs.CL· cs.ET

LLeMpower: Understanding Disparities in the Control and Access of Large Language Models

classification 💻 cs.CY cs.AIcs.CLcs.ET
keywords accessllmsmodelscontrollanguagelargetechnologyadditionally
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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.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Reasoning4Sciences: Bridging Reasoning Language Models to All Scientific Branches

    cs.AI 2026-05 unverdicted novelty 6.0

    Survey of RLM adoption in 28 disciplines reveals maturity disparities via a new assessment framework, with focus on development, evaluation, and public resources.

  2. Reasoning4Sciences: Bridging Reasoning Language Models to All Scientific Branches

    cs.AI 2026-05 unverdicted novelty 6.0

    A survey of RLM use in 28 disciplines reveals uneven adoption and introduces a maturity assessment framework showing larger gaps when limited to public resources.