Text retrieval using learned sparse representations of queries and documents has, over the years, evolved into a highly effective approach to search. It is thanks to recent advances in approximate nearest neighbor search-with the emergence of highly efficient algorithms such as the inverted index-based Seismic and the graph-based Hnsw-that retrieval with sparse representations became viable in practice. In this work, we scrutinize the efficiency of sparse retrieval algorithms and focus particularly on the size of a data structure that is common to all algorithmic flavors and that constitutes a substantial fraction of the overall index size: the forward index. In particular, we seek compression techniques to reduce the storage footprint of the forward index without compromising search quality or inner product computation latency. In our examination with various integer compression techniques, we report that StreamVByte achieves the best trade-off between memory footprint, retrieval accuracy, and latency. We then improve StreamVByte by introducing DotVByte, a new algorithm tailored to inner product computation. Experiments on MsMarco show that our improvements lead to significant space savings while maintaining retrieval efficiency.
Dual Skipping Guidance for Document Retrieval with Learned Sparse Representations
本文提出了一种双跳转指导计划与混合评分，以加速文档检索，使用学习稀疏表示，同时仍然提供了良好的相关性。该方案使用词汇BM 25和学习的神经项权重来分别绑定和组合候选文档的排名得分，以用于跳过和最终排名，并在倒排索引遍历期间保持两个top-k阈值。本文评估的时间效率和排名的相关性所提出的计划在搜索MS MARCO TREC数据集。
Rethinking Soft Compression in Retrieval-Augmented Generation: A Query-Conditioned Selector Perspective
Shanghai Key Laboratory of Data Science, College of Computer Science and Artificial Intelligence（上海数据科学 key 实验室，计算机科学与人工智能学院）；Fudan University（复旦大学）；Shanghai（上海）；China（中国）
TCRA-LLM: Token Compression Retrieval Augmented Large Language Model for Inference Cost Reduction
Meetyou AI Lab（美柚人工智能实验室）；Xiamen Key Laboratory of Women’s Internet Health Management（厦门市女性互联网健康管理重点实验室）；Osaka University（大阪大学）；Agency for Science, Technology and Research (A*STAR)（新加坡科技研究局）
MIT World Peace University（MIT世界和平大学）；Indian Institute of Technology, Hyderabad（印度理工学院海德拉巴分校）；International Center for Theoretical Sciences, Tata Institute of Fundamental Research（塔塔基础科学研究院国际理论科学中心）
