Kernkonzepte
The high computational costs of training and operating large language models have led to a concerning concentration of control and access among the most financially resourced entities, raising ethical concerns about the equitable distribution of this transformative technology.
Zusammenfassung
The study examines the financial and computational requirements for training and operating various large language models (LLMs), ranging from small to large in size. It then compares these requirements to the economic resources of different stakeholders, including private sector organizations, research institutions, and individuals across various countries.
The key findings are:
Training and operating the largest LLMs, such as GPT-4, is financially viable only for a small number of wealthy nations, corporations, and research institutions. The majority of countries and individuals lack the necessary resources to access these powerful models.
This concentration of LLM control and access is reflected in the uneven distribution of model performance across languages, with English-speaking countries dominating the highest levels of performance.
The high costs of LLMs create barriers to entry, limiting the ability of lower-resourced groups, such as newsrooms in Africa, hospitals, and individuals in developing countries, to leverage these transformative technologies.
The study discusses the ethical implications of this inequality, including the potential for LLMs to amplify existing social biases and further exacerbate economic divides if access remains restricted to the wealthy. It proposes several ideas to increase the accessibility of LLMs, such as developing more efficient training and serving methods, encouraging cross-cultural collaborations, and advocating for policies that promote fair access.
Statistiken
Training GPT-4 is estimated to cost $100 million.
Serving GPT-4 for inference costs around $900 per day.
The United States and Canada can train GPT-4 with less than 25% of their total seed fund investments.
Only 4 countries (USA, China, Japan, Germany) can afford to train GPT-4 using less than 0.1% of their national research budgets.
Individuals in the top 56% of countries can afford LLM subscription services if they are willing to spend up to 10% of their monthly income.
Zitate
"The high compute costs of LLMs create a barrier to access for those with lower financial resources."
"If only the rich are able to access LLMs and improve their productivity, will we exacerbate the current inequality in the world economy?"
"Increasing diversity in the LLM research ecosystem may also help researchers gain a deeper understanding of compute constraints within low-resourced communities and the potential impacts of decisions being made during LLM training."