Hypernetworks for Perspectivist Adaptation

2025-10-15

AI Summary

To address parameter redundancy arising from multi-model adaptation in viewpoint-aware classification, this paper proposes a hypernetwork-driven lightweight adaptation architecture. A meta-level hypernetwork dynamically generates viewpoint-specific adapter weights, enabling fine-grained viewpoint alignment in feature space. The approach is architecture-agnostic and plug-and-play, requiring only 0.5%–2% of the original model’s parameters to be fine-tuned for rapid adaptation of diverse foundation models (e.g., BERT, RoBERTa) to heterogeneous user viewpoints. Evaluated on multi-viewbenchmarks—including hate speech and toxicity detection—the method matches the performance of fully fine-tuned viewpoint-specific models while reducing parameter count by 98.3% on average. This substantially alleviates resource bottlenecks in multi-view classification, demonstrating strong efficiency, generalizability, and practical applicability.

Technology Category

• Machine Learning: Multi-instance/Multi-view Learning
• Computer Vision: Large Vision Models
• Natural Language Processing: Safety and Robustness

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Web Mining and Content Analysis: Large pretrained models with web data
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

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.

Problem

• Addressing parametric efficiency bottleneck in perspective-aware classification
• Applying hypernetwork+adapters to perspectivist hate speech detection
• Achieving competitive performance with fewer model parameters

Innovation

• Hypernetworks with adapters for perspectivist classification
• Competes with specialized models using fewer parameters
• Architecture-agnostic solution applicable to various base models