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New QUORUM framework optimizes LLM and human data annotation

By PulseAugur Editorial · [1 sources] ·

Researchers have developed QUORUM, a novel framework designed to optimize data annotation processes for natural language processing tasks. QUORUM dynamically assigns instances to either human annotators or large language models (LLMs) within a specified budget. It differentiates itself by using feature-based signals to estimate instance difficulty, rather than relying on model confidence, and supports combining multiple annotations for improved reliability. Evaluations show QUORUM enhances annotation quality by up to 34.4% while reducing costs by 8.8% compared to existing methods. AI

IMPACT This framework could significantly improve the efficiency and cost-effectiveness of data annotation for LLMs.

RANK_REASON The cluster contains a research paper detailing a new framework for data annotation. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New QUORUM framework optimizes LLM and human data annotation

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The cluster contains a research paper detailing a new framework for data annotation. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Antonio Purificato, Maria Sofia Bucarelli, Andrea Bacciu, Amin Mantrach, Fabrizio Silvestri ·

    QUORUM: QUality-Optimized Routing Using Multiple annotators

    arXiv:2608.27974v1 Announce Type: new Abstract: 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 reliab…

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