Summary of Rank Aggregation in Crowdsourcing For Listwise Annotations, by Wenshui Luo et al.
Rank Aggregation in Crowdsourcing for Listwise Annotations
by Wenshui Luo, Haoyu Liu, Yongliang Ding, Tao Zhou, Sheng wan, Runze Wu, Minmin Lin, Cong Zhang, Changjie Fan, Chen Gong
First submitted to arxiv on: 10 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel approach to rank aggregation through crowdsourcing, specifically addressing the problem of aggregating listwise full ranks across multiple problems. The method, called LAC (Listwise rank Aggregation in Crowdsourcing), incorporates global position information and an annotation quality indicator to measure discrepancies between annotated ranks and true ranks. Additionally, it considers the difficulty of ranking problems themselves, which affects annotator performance and final results. LAC is the first unsupervised method to directly tackle full rank aggregation in listwise crowdsourcing while inferring problem difficulty, annotator ability, and ground-truth ranks. The approach is evaluated on synthetic and real-world datasets, including a business-oriented paragraph ranking dataset, demonstrating its effectiveness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves a big problem in how we use human feedback to train artificial intelligence models. Right now, people are asked to rank things from best to worst, but this task is hard and time-consuming. The researchers propose a new way to make it easier by combining all the rankings together, taking into account what makes some tasks harder than others. They also figure out how well people are doing at ranking and adjust for that. This approach has never been done before and could help us build better AI models. |
Keywords
» Artificial intelligence » Unsupervised
