Information retrieval is a core component of many intelligent systems as it enables conditioning of outputs on new and large-scale datasets. While effective, the standard practice of encoding data into high-dimensional representations for similarity search entails large memory and compute footprints, and also makes it hard to inspect the inner workings of the system. Hierarchical retrieval methods offer an interpretable alternative by organizing data at multiple granular levels, yet do not match the efficiency and performance of flat retrieval approaches. In this paper, we propose Retreever, a tree-based method that makes hierarchical retrieval viable at scale by directly optimizing its structure for retrieval performance while naturally providing transparency through meaningful semantic groupings. Our method offers the flexibility to balance cost and utility by indexing data using representations from any tree level. We show that Retreever delivers strong coarse (intermediate levels) and fine representations (terminal level), while achieving the highest retrieval accuracy at the lowest latency among hierarchical methods. These results demonstrate that this family of techniques is viable in practical applications.

翻译：信息检索作为许多智能系统的核心组件，能够基于新的大规模数据集对输出进行条件化处理。尽管标准做法将数据编码为高维表示以进行相似性搜索是有效的，但这需要大量的内存和计算资源，同时也使得系统内部工作机制难以检视。分层检索方法通过在多粒度级别组织数据，提供了一种可解释的替代方案，但其效率和性能尚未达到扁平检索方法的水平。本文提出Retreever，一种基于树结构的方法，通过直接优化检索性能的结构，使分层检索能够在大规模场景下可行，同时通过有意义的语义分组自然提供透明度。我们的方法通过使用任意树层级的表示来索引数据，提供了平衡成本与效用的灵活性。实验表明，Retreever在提供强大的粗粒度（中间层级）和细粒度（终端层级）表示的同时，在分层检索方法中实现了最高的检索精度和最低的延迟。这些结果证明此类技术在实际应用中是可行的。