IMRNNs: 一种通过嵌入调节实现可解释密集检索的高效方法
IMRNNs: An Efficient Method for Interpretable Dense Retrieval via Embedding Modulation
AI总结：
IMRNNs通过动态嵌入调节提升密集检索的可解释性和检索效果
摘要：
在检索增强生成（RAG）中，黑盒密集检索器的可解释性仍然是一个核心挑战。理解查询和文档之间语义的相互作用对于诊断检索行为和改进模型设计至关重要。然而，现有的密集检索器依赖于静态嵌入来表示查询和文档，这掩盖了这种双向关系。事后方法如重排序器计算成本高，增加了推断延迟，但仍无法揭示底层的语义对齐。为了解决这些限制，我们提出了可解释模块化检索神经网络（IMRNNs），这是一种轻量级框架，可以在推理时通过动态的双向调节来增强任何密集检索器。IMRNNs采用两个独立的适配器：一个将文档嵌入条件地应用于当前查询，另一个利用最初检索到的文档的语料库级反馈来优化查询嵌入。这种迭代调节过程使模型能够动态地调整表示，并暴露查询和文档之间的可解释语义依赖关系。从经验上讲，IMRNNs不仅增强了可解释性，还提高了检索效果。在七个基准数据集上，将我们的方法应用于标准密集检索器，平均在最先进的基线上获得了+6.35%的nDCG、+7.14%的召回率和+7.04%的MRR的提升。这些结果表明，结合可解释性驱动的调节可以同时解释和增强RAG系统中的检索。
英文摘要：
Interpretability in black-box dense retrievers remains a central challenge in Retrieval-Augmented Generation (RAG). Understanding how queries and documents semantically interact is critical for diagnosing retrieval behavior and improving model design. However, existing dense retrievers rely on static embeddings for both queries and documents, which obscures this bidirectional relationship. Post-hoc approaches such as re-rankers are computationally expensive, add inference latency, and still fail to reveal the underlying semantic alignment. To address these limitations, we propose Interpretable Modular Retrieval Neural Networks (IMRNNs), a lightweight framework that augments any dense retriever with dynamic, bidirectional modulation at inference time. IMRNNs employ two independent adapters: one conditions document embeddings on the current query, while the other refines the query embedding using corpus-level feedback from initially retrieved documents. This iterative modulation process enables the model to adapt representations dynamically and expose interpretable semantic dependencies between queries and documents. Empirically, IMRNNs not only enhance interpretability but also improve retrieval effectiveness. Across seven benchmark datasets, applying our method to standard dense retrievers yields average gains of +6.35% nDCG, +7.14% recall, and +7.04% MRR over state-of-the-art baselines. These results demonstrate that incorporating interpretability-driven modulation can both explain and enhance retrieval in RAG systems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
