查询编码器学习困难：向量检索几何易但编码难
原标题：Learning Query Encoders Can Be Hard Even When Vector Retrieval Is Geometrically Easy
AI 摘要
该论文研究向量检索中查询编码器的可学习性问题。作者提出基于ranking SVM的方法评估文档索引的几何容量上限，并在BRIGHT、BEIR、LIMIT等20个数据集上测试多种预训练和微调查询编码器，发现几何容量很少成为瓶颈，但查询编码器即使微调后召回率也常低于50%甚至25%。理论上，作者构造了一个检索任务，证明存在完美召回的小型ReLU网络查询编码器，但任何统计查询学习器都需要指数级查询才能超过随机基线，表明查询编码器的可学习性可能是密集检索的主要障碍。
正文节选
Learning Query Encoders Can Be Hard Even When Vector Retrieval Is Geometrically Easy Abstract Efficient vector retrieval requires both a corpus geometry that supports retrieving the right documents through vector similarity, and a query encoder that can embed queries near their desired documents in the embedding space. Recent work has studied geometric capacity through the lens of the minimum embedding dimension needed to realize all top- answer sets of documents. We study a different notion of