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New algorithmic framework uses LLMs to optimize crowdsourcing contest selection

By PulseAugur Editorial · [1 sources] ·

Researchers have developed GRAF, a new algorithmic framework designed to optimize worker self-selection in crowdsourcing contests. GRAF aims to ensure that important contests receive adequate participation and effort, while also minimizing worker regret by recommending suitable contests. To address the complexity of designing effective scoring algorithms for heterogeneous workers, an LLM-driven evolutionary framework called LLMScore was introduced. This framework can jointly optimize platform utility and worker satisfaction, and has demonstrated strong performance and transferability across different settings. AI

IMPACT This research could lead to more efficient and fair crowdsourcing platforms by optimizing worker participation and satisfaction.

RANK_REASON The cluster contains an academic paper detailing a new algorithmic approach and framework for crowdsourcing. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv cs.LG →

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

New algorithmic framework uses LLMs to optimize crowdsourcing contest selection

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The cluster contains an academic paper detailing a new algorithmic approach and framework for crowdsourcing. [lever_c_demoted from research: ic=1 ai=0.7]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Nguyen Thach, Hau Chan, David Parkes, Karim Lakhani ·

    Guiding Worker Self-Selection in Crowdsourcing Contests: An LLM-Augmented Algorithmic Approach

    arXiv:2609.07749v1 Announce Type: new Abstract: Crowdsourcing platforms coordinate large pools of online workers who strategically choose which contests to enter and how much effort to invest. This self-selection can leave important contests with too few participants or too littl…

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