Neural models are increasingly used in Web-scale Information Retrieval (IR). However, relying on these models introduces substantial computational and energy requirements, leading to increasing attention toward their environmental cost and the sustainability of large-scale deployments. While neural IR models deliver high retrieval effectiveness, their scalability is constrained in multi-domain scenarios, where training and maintaining domain-specific models is inefficient and achieving robust cross-domain generalisation within a unified model remains difficult. This paper introduces DRAMA (Domain Retrieval using Adaptive Module Allocation), an energy- and parameter-efficient framework designed to reduce the environmental footprint of neural retrieval. DRAMA integrates domain-specific adapter modules with a dynamic gating mechanism that selects the most relevant domain knowledge for each query. New domains can be added efficiently through lightweight adapter training, avoiding full model retraining. We evaluate DRAMA on multiple Web retrieval benchmarks covering different domains. Our extensive evaluation shows that DRAMA achieves comparable effectiveness to domain-specific models while using only a fraction of their parameters and computational resources. These findings show that energy-aware model design can significantly improve scalability and sustainability in neural IR.

翻译：神经网络模型在Web规模信息检索中的应用日益广泛。然而，依赖这些模型会带来巨大的计算和能源需求，引发了对其环境成本和大规模部署可持续性的日益关注。尽管神经检索模型能够实现较高的检索效能，但其可扩展性在多领域场景中受到限制：训练和维护领域专用模型效率低下，而在统一模型内实现稳健的跨领域泛化仍然困难。本文提出DRAMA（基于自适应模块分配的领域检索），这是一种能源高效且参数高效的框架，旨在降低神经检索的环境足迹。DRAMA将领域专用适配器模块与动态门控机制相结合，该机制能够为每个查询选择最相关的领域知识。新领域可通过轻量级适配器训练高效添加，无需进行完整模型重训练。我们在涵盖不同领域的多个Web检索基准上评估DRAMA。大量实验表明，DRAMA仅使用领域专用模型一小部分的参数和计算资源，即可达到与之相当的检索效能。这些发现表明，具备能源意识的模型设计能够显著提升神经检索的可扩展性与可持续性。