Improving Retrieval in Large Language Models by Rethinking Similarity and Diversity with Sum Vectors

This paper proposes a novel approach to vector retrieval in large language models (LLMs) that leverages the concept of sum vectors to simultaneously optimize for similarity and diversity, addressing the limitations of traditional methods like Maximal Marginal Relevance (MMR).