Large language models and text encoders increasingly power web-based information systems in the social sciences, including digital libraries, data catalogues, and search interfaces used by researchers, policymakers, and civil society. Full fine-tuning is often computationally and energy intensive, which can be prohibitive for smaller institutions and non-profit organizations in the Web4Good ecosystem. Parameter-Efficient Fine-Tuning (PEFT), especially Low-Rank Adaptation (LoRA), reduces this cost by updating only a small number of parameters. We show that the standard LoRA update $ΔW = BA^\top$ has geometric drawbacks: gauge freedom, scale ambiguity, and a tendency toward rank collapse. We introduce OrthoGeoLoRA, which enforces an SVD-like form $ΔW = BΣA^\top$ by constraining the low-rank factors to be orthogonal (Stiefel manifold). A geometric reparameterization implements this constraint while remaining compatible with standard optimizers such as Adam and existing fine-tuning pipelines. We also propose a benchmark for hierarchical concept retrieval over the European Language Social Science Thesaurus (ELSST), widely used to organize social science resources in digital repositories. Experiments with a multilingual sentence encoder show that OrthoGeoLoRA outperforms standard LoRA and several strong PEFT variants on ranking metrics under the same low-rank budget, offering a more compute- and parameter-efficient path to adapt foundation models in resource-constrained settings.

翻译：大型语言模型与文本编码器日益成为社会科学领域网络信息系统的核心驱动力，涵盖数字图书馆、数据目录以及供研究人员、政策制定者和公民社会使用的检索界面。全参数微调通常在计算和能耗上成本高昂，这对Web4Good生态中的小型机构与非营利组织构成显著障碍。参数高效微调（PEFT），尤其是低秩自适应（LoRA），通过仅更新少量参数来降低这一成本。本文指出标准LoRA更新$ΔW = BA^\top$存在几何缺陷：规范自由度、尺度模糊性以及秩崩溃倾向。我们提出OrthoGeoLoRA，通过约束低秩因子为正交矩阵（Stiefel流形），强制其具有类SVD形式$ΔW = BΣA^\top$。几何重参数化方法在保持与Adam等标准优化器及现有微调流程兼容性的同时实现了该约束。我们还构建了一个基于欧洲语言社会科学叙词表（ELSST）的层次化概念检索基准，该叙词表被广泛用于数字资源库中社会科学资源的组织。在多语言句子编码器上的实验表明，在相同低秩预算下，OrthoGeoLoRA在排序指标上优于标准LoRA及多种强效PEFT变体，为资源受限场景中基础模型的适配提供了更高效的计算与参数优化路径。