QUORUM: QUality-Optimized Routing Using Multiple annotators
Data annotation remains a central bottleneck in natural language processing, requiring human effort to obtain high-quality labels at scale. While Large Language Models (LLMs) offer a fast and cost-effective alternative, their reliability is highly instance-dependent: they perform well on simple inputs but often fail on examples requiring nuanced reasoning or contextual understanding. In this work, we address this challenge with QUORUM (QUality-Optimized Routing Using Multiple annotators), a budget-aware routing framework that dynamically assigns each instance to human or LLM annotators under a fixed annotation budget. Unlike prior approaches relying on model confidence or uncertainty esti...
| Authors | Antonio Purificato, Maria Sofia Bucarelli, Andrea Bacciu et al. |
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| Primary category | cs.CL |
| Published | 28 August 2026 |
| Last updated at source | 28 August 2026 |
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