Cohere's Head of Machine Learning, Nils Reimers, discussed the evolution of embedding models at Cohere during the second edition of Vector Space Talks, highlighting several key developments. He emphasized the importance of content quality estimation in embeddings, which goes beyond traditional topic matching to differentiate between informative and non-informative documents. Nils also introduced compression-aware training to reduce the memory footprint of embeddings, making them more cost-effective for platforms like Qdrant. By applying reinforcement learning from human feedback, Cohere's models are able to learn preferences and produce more informative responses. He stressed the significance of evaluating embedding quality in relative terms, focusing on the context and relationships between embeddings. Additionally, Nils provided insights into upcoming features, such as input type support for Langchain and advanced compression techniques, while noting the challenge in differentiating true from fake statements due to reliance on pretraining data.