An adaptive two-phase semantic filter using clustering then a hybrid proxy trained on LLM confidence achieves 1.6-2.0x speedup over prior methods at 90% accuracy on 10K document corpora.
Confident learning: Estimating uncertainty in dataset labels.Journal of Artificial Intelligence Research, 70:1373–1411, Apr 2021
4 Pith papers cite this work, alongside 61 external citations. Polarity classification is still indexing.
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LasRepair++ pairs an LLM instructor with an SLM corrector, refines context via EM, and down-weights uncertain repairs using column-calibrated confidence, reporting 18.1% average F1 gain over baselines on data repair tasks.
Global analysis of LIS papers finds country-specific research method profiles with differences between national and international distributions narrowing over 30 years.
LipB-ViT adds bi-Lipschitz Bayesian layers to vision transformers and uses uncertainty-aware fusion to identify corrupted labels with over 93% recall at 15% noise, beating kNN baselines.
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Fast LLM-Based Semantic Filtering: From a Unified Framework to an Adaptive Two-Phase Method
An adaptive two-phase semantic filter using clustering then a hybrid proxy trained on LLM confidence achieves 1.6-2.0x speedup over prior methods at 90% accuracy on 10K document corpora.
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Collaborative Large and Small Language Models for Accurate and Scalable Data Repair
LasRepair++ pairs an LLM instructor with an SLM corrector, refines context via EM, and down-weights uncertain repairs using column-calibrated confidence, reporting 18.1% average F1 gain over baselines on data repair tasks.
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Non-synchronism in Global Usage of Research Methods in Library and Information Science from 1990 to 2019
Global analysis of LIS papers finds country-specific research method profiles with differences between national and international distributions narrowing over 30 years.
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Architecture-agnostic Lipschitz-constant Bayesian header and its application to resolve semantically proximal classification errors with vision transformers
LipB-ViT adds bi-Lipschitz Bayesian layers to vision transformers and uses uncertainty-aware fusion to identify corrupted labels with over 93% recall at 15% noise, beating kNN baselines.