Aug 7, 2024 · Crowdsourcing deals with solving problems by assigning them to a large number of non-experts called crowd using their spare time.
Annotator-Centric Active Learning for Subjective NLP Tasks - arxiv-sanity
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Jul 19, 2024 · A common practice in building NLP datasets, especially using crowd-sourced ... Large-scale datasets are important for the development of deep learning models.
6 days ago · We propose a novel collaborative learning framework termed NoiseAL, which employs. SMs as filters to segment noisy data and LLMs as active annotators without ...
Jul 18, 2024 · Crowdsourcing is a popular method used to estimate ground-truth labels by collecting noisy labels from workers. In this work, we are motivated by crowdsourcing ...
7 days ago · an analysis of the limits of scaling datasets in machine learning. ArXiv ... Scaling relationship on learning mathematical reasoning with large language models.
Jul 24, 2024 · ... large, labeled datasets for quality control validation are much harder to acquire. One potential solution is to augment existing labels using crowd sourcing.
6 days ago · In light of this, this paper aims to utilize the Kaggle case to deeply analyze the realization process and intrinsic mechanisms of value co-creation in ...
Jul 17, 2024 · We primarily question whether the use of large Web-scraped datasets should be viewed as differential- privacy-preserving. We caution that publicizing these ...
Aug 2, 2024 · Concentrating AI research, particularly in deep learning (DL), on big datasets exacerbates AI inequality, as tech giants such as Meta, Amazon, Apple, Netflix ...
Aug 3, 2024 · ALLIE: Active Learning on Large-scale Imbalanced Graphs Limeng Cui, Xianfeng Tang, Sumeet Katariya, Nikhil Rao, Pallav Agrawal, Karthik Subbian, Dongwon Lee