English(EN) Guiding Worker Self-Selection in Crowdsourcing Contests: An LLM-Augmented Algorithmic Approach

新的算法框架使用LLM优化众包竞赛选择

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-10 04:00

研究人员开发了GRAF，一个旨在优化众包竞赛中工人自我选择的新算法框架。GRAF旨在确保重要竞赛获得充分的参与和努力，同时通过推荐合适的竞赛来最小化工人的遗憾。为了解决为异构工人设计有效评分算法的复杂性，引入了一个由LLM驱动的进化框架LLMScore。该框架可以联合优化平台效用和工人满意度，并在不同环境中展示了强大的性能和可转移性。 AI

影响这项研究通过优化工人的参与度和满意度，可能带来更高效和公平的众包平台。

排序理由该集群包含一篇学术论文，详细介绍了众包的新算法方法和框架。[lever_c_demoted from research: ic=1 ai=0.7]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

本文如何被排名

Signal score: 9 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 该集群包含一篇学术论文，详细介绍了众包的新算法方法和框架。[lever_c_demoted from research: ic=1 ai=0.7]
Source corroboration: Single-source cluster; Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics: paper, product; Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance: High; Clearly on-topic for AI-industry coverage.
Story freshness: Breaking (< 6h); Fresh story with cross-source coverage still developing. Ranking may shift as more sources report.

完整方法见我们的编辑标准。

报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Nguyen Thach, Hau Chan, David Parkes, Karim Lakhani · 2026-09-10 04:00

众包竞赛中引导工人自我选择：一种大型语言模型增强的算法方法

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…

本文如何被排名

报道来源 [1]

众包竞赛中引导工人自我选择：一种大型语言模型增强的算法方法

相关实体

相关话题