Large Language Models (LLMs) have shown incredible promise in understanding and generating text across multiple languages. Yet, inconsistencies arise when identical queries are presented in different languages, hindering their full potential. Imagine asking a question in English and getting a different answer than if you asked the same question in Chinese. This discrepancy isn’t just a translation issue; it reveals underlying gaps and conflicts in the LLM’s knowledge base. Researchers are tackling this challenge head-on, exploring how to aggregate knowledge across languages to create more consistent and comprehensive understanding. One promising approach involves using a "low-resource knowledge detector." This tool identifies when a query involves information that is underrepresented in a specific language. If such a gap is detected, the system strategically selects a "target language" where the information is more readily available. The query is then translated, processed in the target language, and the resulting answer is integrated with or replaces the original response. This process helps LLMs leverage their strengths across different languages, filling in knowledge gaps and reducing inconsistencies. Early experiments show significant improvements, particularly in minimizing performance disparities between languages. This not only enhances accuracy but also promotes fairness, ensuring that users from diverse linguistic backgrounds have equal access to information. While challenges remain, such as the need for language-specific knowledge detectors and managing potentially conflicting information between languages, this research opens exciting avenues for developing truly multilingual LLMs that can seamlessly bridge language barriers and unlock a world of interconnected knowledge.