Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Efficient Online Crowdsourcing with Complex Annotations
Authors: Reshef Meir, Viet-An Nguyen, Xu Chen, Jagdish Ramakrishnan, Udi Weinsberg
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive evaluations on real-world crowdsourcing data from Meta and show the effectiveness of our proposed online algorithms in improving the cost-quality trade-off. and To showcase the effectiveness of our proposed methods, we conduct extensive numerical experiments on four complex annotation datasets and compare against several baselines. |
| Researcher Affiliation | Collaboration | Reshef Meir1,*, Viet-An Nguyen2, Xu Chen2, Jagdish Ramakrishnan2, Udi Weinsberg2 1Technion Israel Institute of Technology 2Central Applied Science, Meta |
| Pseudocode | Yes | Algorithm 1: OAK (LEARNING COMPONENT), Algorithm 2: OAK (ESTIMATION COMPONENT), Algorithm 3: POAK (ESTIMATION COMPONENT) |
| Open Source Code | No | The paper does not provide an explicit statement or a link indicating that the source code for the methodology is openly available. |
| Open Datasets | No | All methods are applied on four real crowdsourcing datasets obtained from Meta, covering a broad range of different labeling tasks. See basic information of the datasets in Tab. 3. The paper does not provide concrete access information (link, DOI, formal citation with authors/year) for these datasets. |
| Dataset Splits | No | We first randomly split each dataset into a training set and a test set. The paper explicitly mentions training and test sets, but not a separate validation set or the specific percentages/counts for the splits to reproduce the partitioning. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependency details with version numbers. |
| Experiment Setup | No | The paper mentions a meta-parameter 'γ = 10' and implies some aggregation function and thresholds are decided, but it does not provide specific details on hyperparameters, training configurations, or system-level settings for the experiments. |