EVian decomposes vision-language model responses into three cognitive components and audits them along consistency, coherence, and accuracy axes, showing that a small curated subset outperforms much larger training sets.
Unmasking and improving data credibility: A study with datasets for training harmless language models
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UNVERDICTED 4representative citing papers
The ADC method automates the creation of large image classification datasets using LLMs and search engines, achieving 79% human agreement and reducing label noise on a 1 million image clothing dataset, while also releasing benchmarks for noise and bias issues.
Relabeler is an end-to-end framework that detects corrupted labels via local and global instance relationships and corrects them using feature-based estimation, reporting up to 58% better label correction precision than baselines.
CANOLA estimates label noise and performs cautious iterative soft-label refinement to correct corrupted training data, reporting 19-52% error reduction versus prior methods on six datasets.
citing papers explorer
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Evian: Towards Explainable Visual Instruction-tuning Data Auditing
EVian decomposes vision-language model responses into three cognitive components and audits them along consistency, coherence, and accuracy axes, showing that a small curated subset outperforms much larger training sets.
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Automatic Dataset Construction (ADC): Sample Collection, Data Curation, and Beyond
The ADC method automates the creation of large image classification datasets using LLMs and search engines, achieving 79% human agreement and reducing label noise on a 1 million image clothing dataset, while also releasing benchmarks for noise and bias issues.
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A Data-Centric Framework for Detecting and Correcting Corrupted Labels
Relabeler is an end-to-end framework that detects corrupted labels via local and global instance relationships and corrects them using feature-based estimation, reporting up to 58% better label correction precision than baselines.
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Noise-Aware Framework for Correcting Corrupted Labels
CANOLA estimates label noise and performs cautious iterative soft-label refinement to correct corrupted training data, reporting 19-52% error reduction versus prior methods on six datasets.