Access to the academic research field is frequently impeded by language barriers and the overwhelming volume of available information, which together limit the dissemination and impact of scholarly work, especially for non-English speaking researchers. This challenge has led to the development of research silos where groups of researchers around the world, may have limited interactions, and even biases, with the research written in their non-native languages. There is a critical need for innovative solutions that bridge this language divides. This study presents a method for developing and evaluating the performance of Large Language Models (LLMs) based agents as foreign research interpreters and suggests we can use Fairness Metrics to monitor its performance as an interpreter across distinct language-separated communities. The findings offer valuable insights into leveraging technology to overcome accessibility barriers, contributing to more inclusive dissemination of academic knowledge.