The Differentiable Search Index (DSI) is a novel information retrieval (IR) framework that utilizes a differentiable function to generate a sorted list of document identifiers in response to a given query. However, due to the black-box nature of the end-to-end neural architecture, it remains to be understood to what extent DSI possesses the basic indexing and retrieval abilities. To mitigate this gap, in this study, we define and examine three important abilities that a functioning IR framework should possess, namely, exclusivity, completeness, and relevance ordering. Our analytical experimentation shows that while DSI demonstrates proficiency in memorizing the unidirectional mapping from pseudo queries to document identifiers, it falls short in distinguishing relevant documents from random ones, thereby negatively impacting its retrieval effectiveness. To address this issue, we propose a multi-task distillation approach to enhance the retrieval quality without altering the structure of the model and successfully endow it with improved indexing abilities. Through experiments conducted on various datasets, we demonstrate that our proposed method outperforms previous DSI baselines.

翻译：可微分搜索索引（DSI）是一种新颖的信息检索（IR）框架，它利用可微分函数根据给定查询生成排序后的文档标识符列表。然而，由于端到端神经架构的黑箱特性，DSI在多大程度上具备基本的索引和检索能力仍有待理解。为弥补这一不足，本研究定义并考察了功能完善的IR框架应具备的三项重要能力，即排他性、完备性和相关性排序。我们的分析实验表明，虽然DSI在记忆从伪查询到文档标识符的单向映射方面表现出色，但在区分相关文档与随机文档方面存在不足，从而对其检索效果产生负面影响。为解决这一问题，我们提出了一种多任务蒸馏方法，在不改变模型结构的情况下提升检索质量，并成功赋予其改进的索引能力。通过在多个数据集上的实验，我们证明了所提出的方法优于以往的DSI基线方法。