发表机构
University of Milano-Bicocca; Bielefeld University（米兰比可卡大学; 比勒费尔德大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
ExpertLens提出一种针对MoE增强密集检索器的事后可解释框架，通过嵌入空间可视化和概念激活向量，揭示专家路由对检索效果的影响，实验验证其能改善嵌入空间结构。
AI 中文摘要
包括密集检索器在内的神经模型已在信息检索（IR）领域被广泛采用，通常能带来最先进的性能。尽管这些模型效果显著，但它们作为黑箱运行，限制了其排序决策的可解释性。现有的针对神经排序器的事后可解释性方法主要关注特征级归因，这可能不足以捕捉学习到的嵌入空间的复杂性。在这项工作中，我们提出了ExpertLens，一个针对混合专家（MoE）增强的密集检索器的事后可解释性框架，它将焦点从局部标量特征重要性转移到表示级别的全局可解释性。ExpertLens利用判别性嵌入空间可视化，并结合自动提取的概念激活向量，来揭示专家路由如何驱动嵌入空间的形成和检索效果。在五个IR基准测试和两个MoE增强的密集检索器上的实验表明，专家路由持续改善嵌入空间结构，将查询及其相关文档定位到更明确的几何邻域中。对专家子空间的分析进一步揭示了通用型主导专家，以及表现出明显语言专业化的少数专家，子空间根据多语义概念相似性进行排列。我们的代码已公开可用。
英文摘要
Neural models, including dense retrievers, have been widely adopted in Information Retrieval (IR), often delivering state-of-the-art performance. Despite their effectiveness, these models operate as black boxes, limiting the interpretability of their ranking decisions. Existing post-hoc explainability methods for neural rankers primarily focus on feature-level attributions, which can be insufficient to capture the complexity of learned embedding spaces. In this work, we propose ExpertLens, a post-hoc explainability framework for Mixture-of-Experts (MoE)-enhanced dense retrievers that shifts focus from local scalar feature importance to representation-level global interpretability. ExpertLens leverages discriminative embedding space visualizations jointly with automatically extracted Concept Activation Vectors to reveal how expert routing drives embedding space formulation and retrieval effectiveness. Experiments across five IR benchmarks and two MoE-enhanced dense retrievers show that expert routing consistently improves embedding space structure, positioning queries and their relevant documents into better-defined geometric neighborhoods. Analysis of expert subspaces further reveals general-purpose dominant experts, along with minority experts exhibiting distinct linguistic specialization, with subspaces arranged according to multi-semantic concept similarity. Our code is publicly available.
