The Impact of Large Language Models (LLMs) on Local Cultures and Languages

Large Language Models (LLMs) such as GPT-4, LLaMA, Gemini and others have revolutionized how we interact with technology. These models, capable of understanding and generating human-like text, have found numerous applications in the business and private lives of billions of people. However, the increasing prevalence of LLMs poses significant challenges to local cultures and languages (non-English-based), often overlooked in the race for technological advancement. More than 90% of the most popular LLMs are trained on English language data and text related to English-based culture. This presentation will delve into the risks associated with LLMs, including the spread of misinformation, built-in biases, privacy breaches, and their potential negative impact on local cultures and languages. We will explore how LLMs, trained on vast datasets that include both accurate and inaccurate information, can inadvertently disseminate false or misleading content, particularly in local contexts where resources to verify facts may be limited. Additionally, the inherent biases in LLMs, stemming from the data they are trained on, can marginalize local cultures and languages, leading to their erosion. Privacy breaches are another critical issue, as LLMs can inadvertently reveal sensitive information, undermining trust in technology. Moreover, LLMs can be a direct threat to local cultures and languages as they are not native languages but are translated, often stripping away the nuances and subtleties integral to cultural expression. The potential for LLMs to be used as tools for manipulation, spreading political, religious, and cultural biases embedded in the data further exacerbates these risks. We will examine real-world examples, discuss current methods of addressing these problems, and engage with world-renowned academics, bestselling authors, and business practitioners to propose safer use and deployment rules for LLMs. Our goal is to help participants better understand and manage LLM challenges at the level of families, companies, and countries, aligning with several United Nations Sustainable Development Goals (SDGs), and fostering a more inclusive and equitable digital future. The session will also examine the regulatory and policy-enabling environment necessary to address LLM-oriented risks, with particular reference to less developed nations.