Cultural values and traditions differ across the globe, but large language models (LLMs), used in text-generating programs such as ChatGPT, have a tendency to reflect values from English-speaking and Protestant European countries. A Cornell University-led research team believes there is an easy way to solve that problem.
The researchers tested five versions of ChatGPT and found that what they call “cultural prompting” — asking the AI model to perform a task like someone from another part of the world — resulted in reduced bias in responses for the vast majority of the over 100 countries they tested. The findings suggest there could be an easy way for anyone to control AI models to align with cultural values, they said, to reduce the cultural bias in these widely used systems.
“There are not many organizations in the world with the capacity to build large language models, because it’s highly resource intensive, so it’s all the more important for the ones that do have that power, and therefore responsibility, to carefully consider how their models might affect different parts of the world,” said Rene Kizilcec, associate professor of information science.
“Around the world, people are using tools like ChatGPT directly and indirectly via other applications for learning, work, and communication,” he said, “and just like technology companies make localized keyboards on laptops to adapt to different languages, LLMs need to adapt to different cultural norms and values.”
Kizilcec is senior author of “Cultural Bias and Cultural Alignment of Large Language Models,” which published Sept. 17 in PNAS Nexus. The lead author is Yan Tao, doctoral student in the field of information science and a member of Kizilcec’s Future of Learning Lab.
For their research, Kizilcec and his team tested five versions of ChatGPT — 3, 3.5 Turbo, 4, 4-Turbo and 4o, the latter released in May — and compared the models’ responses to nationally representative survey data from the Integrated Values Survey, an established measure of cultural values for 107 countries and territories.
For the most recent models tested (GPT-4, 4-turbo, 4o), the latter prompting improved cultural alignment for 71% to 81% of countries and territories.
“Unlike fine-tuning models or using prompts in different languages to elicit language-specific cultural values — which typically require specialized resources — cultural prompting merely involves specifying a cultural identity directly in the prompts,” Tao said. “This approach is more user-friendly and does not demand extensive resources.”
This research was funded in part by the Jacobs Foundation and Digital Futures.
