arXivDaily arXiv每日学术速递 周一至周五更新
arXiv周末暂无论文更新，休息一下吧，周末愉快~~
arXiv 2602.14960cs.IR
收藏

DRAMA：基于自适应模块分配的领域检索

EMAR: Efficient Multi-domain Adaptive Retrieval

University of Milano-Bicocca（米兰比可卡大学）；ISTI-CNR（意大利国家研究委员会信息系统技术研究所）；Politecnico di Torino（都灵理工大学）；Georgetown University（乔治城大学）

Pranav Kasela, Marco Braga, Ophir Frieder, Nazli Goharian, Gabriella Pasi, Raffaele Perego

阅读 PDF
arXiv 页面 ↗HTML 原文 ↗

AI总结：

DRAMA通过自适应模块分配实现高效领域检索，减少能耗与参数使用，提升神经检索的可持续性。

摘要：

神经模型正越来越多地应用于网络级信息检索（IR）。然而，依赖这些模型会带来显著的计算和能源需求，导致对它们的环境成本和大规模部署可持续性的关注日益增加。尽管神经IR模型在检索效果上表现优异，但在多领域场景中，其可扩展性受到限制，因为训练和维护领域特定模型效率低下，且在统一模型中实现稳健的跨领域泛化仍然困难。本文介绍了DRAMA（基于自适应模块分配的领域检索），一种能耗和参数高效的设计框架，旨在减少神经检索的环境足迹。DRAMA集成了领域特定的适配器模块，并采用动态门控机制，选择每个查询最相关的领域知识。新领域可通过轻量级适配器训练高效添加，避免全模型重新训练。我们在多个网络检索基准上评估了DRAMA，覆盖不同领域。我们的广泛评估显示，DRAMA在效果上与领域特定模型相当，但仅使用其参数和计算资源的很小一部分。这些发现表明，能源意识的模型设计可以显著提高神经IR的可扩展性和可持续性。

英文摘要：

Neural retrieval systems are increasingly deployed in multi-domain settings, where each domain may have its own vocabulary and word distribution. A common approach is to fine-tune separate models for each domain, achieving strong in-domain performance while increasing training costs and storage requirements. In contrast, a single model trained jointly across domains avoids model duplication but weakens robustness under domain shift. This creates a trade-off between effectiveness and resource-efficiency. To address this issue, we propose EMAR, a retrieval system that employs a single frozen model shared across all domains and introduces lightweight domain-specific adapters. At inference time, a learned routing mechanism dynamically selects the most suitable adapter for each query. EMAR enables efficient domain-specific retrieval by sharing the computationally expensive backbone across multiple domains and without maintaining multiple full models. Experimental results on three multi-domain benchmarks spanning seventeen domains and three retrieval architectures show that EMAR achieves retrieval effectiveness on par or even better than fully fine-tuned domain-specific models while requiring storing only a small fraction of parameters per domain. Furthermore, EMAR exhibits strong generalization to previously unseen domains, highlighting its effectiveness as an extensible solution for multi-domain retrieval.

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。

↑