A Dataset for the Validation of Truth Inference Algorithms Suitable for Online Deployment

• URL: http://arxiv.org/abs/2403.08826v1
• Date: Sun, 10 Mar 2024 16:00:41 GMT
• Title: A Dataset for the Validation of Truth Inference Algorithms Suitable for Online Deployment
• Authors: Fei Wang, Haoyu Liu, Haoyang Bi, Xiangzhuang Shen, Renyu Zhu, Runze Wu, Minmin Lin, Tangjie Lv, Changjie Fan, Qi Liu, Zhenya Huang, Enhong Chen,
• Abstract summary: We introduce a substantial crowdsourcing annotation dataset collected from a real-world crowdsourcing platform. This dataset comprises approximately two thousand workers, one million tasks, and six million annotations. We evaluate the effectiveness of several representative truth inference algorithms on this dataset.
• Score: 76.04306818209753
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: For the purpose of efficient and cost-effective large-scale data labeling, crowdsourcing is increasingly being utilized. To guarantee the quality of data labeling, multiple annotations need to be collected for each data sample, and truth inference algorithms have been developed to accurately infer the true labels. Despite previous studies having released public datasets to evaluate the efficacy of truth inference algorithms, these have typically focused on a single type of crowdsourcing task and neglected the temporal information associated with workers' annotation activities. These limitations significantly restrict the practical applicability of these algorithms, particularly in the context of long-term and online truth inference. In this paper, we introduce a substantial crowdsourcing annotation dataset collected from a real-world crowdsourcing platform. This dataset comprises approximately two thousand workers, one million tasks, and six million annotations. The data was gathered over a period of approximately six months from various types of tasks, and the timestamps of each annotation were preserved. We analyze the characteristics of the dataset from multiple perspectives and evaluate the effectiveness of several representative truth inference algorithms on this dataset. We anticipate that this dataset will stimulate future research on tracking workers' abilities over time in relation to different types of tasks, as well as enhancing online truth inference.

Related papers

• Approaching Metaheuristic Deep Learning Combos for Automated Data Mining [0.5419570023862531]
  This work proposes a means of combining meta-heuristic methods with conventional classifiers and neural networks in order to perform automated data mining. Experiments on the MNIST dataset for handwritten digit recognition were performed. It was empirically observed that using a ground truth labeled dataset's validation accuracy is inadequate for correcting labels of other previously unseen data instances.
  arXiv  Detail & Related papers  (2024-10-16T10:28:22Z)
• A CLIP-Powered Framework for Robust and Generalizable Data Selection [51.46695086779598]
  Real-world datasets often contain redundant and noisy data, imposing a negative impact on training efficiency and model performance. Data selection has shown promise in identifying the most representative samples from the entire dataset. We propose a novel CLIP-powered data selection framework that leverages multimodal information for more robust and generalizable sample selection.
  arXiv  Detail & Related papers  (2024-10-15T03:00:58Z)
• Multi-News+: Cost-efficient Dataset Cleansing via LLM-based Data Annotation [9.497148303350697]
  We present a case study that extends the application of LLM-based data annotation to enhance the quality of existing datasets through a cleansing strategy. Specifically, we leverage approaches such as chain-of-thought and majority voting to imitate human annotation and classify unrelated documents from the Multi-News dataset.
  arXiv  Detail & Related papers  (2024-04-15T11:36:10Z)
• Stochastic Amortization: A Unified Approach to Accelerate Feature and Data Attribution [62.71425232332837]
  We show that training amortized models with noisy labels is inexpensive and surprisingly effective. This approach significantly accelerates several feature attribution and data valuation methods, often yielding an order of magnitude speedup over existing approaches.
  arXiv  Detail & Related papers  (2024-01-29T03:42:37Z)
• An Algorithm for Streaming Differentially Private Data [7.726042106665366]
  We derive an algorithm for differentially private synthetic streaming data generation, especially curated towards spatial datasets. The utility of our algorithm is verified on both real-world and simulated datasets.
  arXiv  Detail & Related papers  (2024-01-26T00:32:31Z)
• Algorithmic Fairness Datasets: the Story so Far [68.45921483094705]
  Data-driven algorithms are studied in diverse domains to support critical decisions, directly impacting people's well-being. A growing community of researchers has been investigating the equity of existing algorithms and proposing novel ones, advancing the understanding of risks and opportunities of automated decision-making for historically disadvantaged populations. Progress in fair Machine Learning hinges on data, which can be appropriately used only if adequately documented. Unfortunately, the algorithmic fairness community suffers from a collective data documentation debt caused by a lack of information on specific resources (opacity) and scatteredness of available information (sparsity)
  arXiv  Detail & Related papers  (2022-02-03T17:25:46Z)
• Bayesian Semi-supervised Crowdsourcing [71.20185379303479]
  Crowdsourcing has emerged as a powerful paradigm for efficiently labeling large datasets and performing various learning tasks. This work deals with semi-supervised crowdsourced classification, under two regimes of semi-supervision.
  arXiv  Detail & Related papers  (2020-12-20T23:18:51Z)
• Learning to Count in the Crowd from Limited Labeled Data [109.2954525909007]
  We focus on reducing the annotation efforts by learning to count in the crowd from limited number of labeled samples. Specifically, we propose a Gaussian Process-based iterative learning mechanism that involves estimation of pseudo-ground truth for the unlabeled data.
  arXiv  Detail & Related papers  (2020-07-07T04:17:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.