用超网络+适配器实现高效视角化分类，参数少还能媲美专用模型。
Hypernetworks for Perspectivist Adaptation
- 采用超网络与适配器组合，提升视角感知分类的参数效率。
- 在仇恨言论检测中性能接近专用模型，参数量显著减少。
- 无需修改主模型，适配多种基线架构，即插即用。
视角感知分类任务存在参数效率瓶颈，但现有研究对此关注不足。本文将超网络+适配器结构引入视角化分类，提出一种高效解决方案。该方法在用户视角下的仇恨言论与毒性检测任务中，性能可媲美专用模型，同时显著降低参数量。方案具备架构无关性，可直接应用于多种基础模型，无需额外设计或训练。
原文摘要 · Abstract (English)
The task of perspective-aware classification introduces a bottleneck in terms of parametric efficiency that did not get enough recognition in existing studies. In this article, we aim to address this issue by applying an existing architecture, the hypernetwork+adapters combination, to perspectivist classification. Ultimately, we arrive at a solution that can compete with specialized models in adopting user perspectives on hate speech and toxicity detection, while also making use of considerably fewer parameters. Our solution is architecture-agnostic and can be applied to a wide range of base models out of the box.
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