The field of natural language processing is moving towards a more equitable and inclusive approach to knowledge sharing across languages. Recent developments have highlighted the importance of surfacing complementary information from non-English language editions and leveraging language transfer to improve low-resource language technologies. Innovations in adapter methods and cross-attention fine-tuning have shown promise in improving performance in low-resource languages, although the benefits of these methods may lie in parameter regularization rather than meaningful information transfer. Additionally, research has challenged the assumption that multilingual training is necessary or beneficial for effective transfer in sense-aware tasks, instead highlighting the importance of rigorous evaluations and fine-tuning data composition. Noteworthy papers include WikiGap, which enables access to complementary information from non-English Wikipedia editions, and Limited-Resource Adapters Are Regularizers, Not Linguists, which investigates the role of adapters in low-resource language technologies. Multilingual Information Retrieval with a Monolingual Knowledge Base also presents a novel strategy for fine-tuning multilingual embedding models, enabling multilingual information retrieval with a monolingual knowledge base.