Papers with LNL
NoisywikiHow: A Benchmark for Learning with Real-world Noisy Labels in Natural Language Processing (2023.findings-acl)
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| Challenge: | Large-scale datasets in the real world often contain label noise, which can cause model overfitting and degrade generalization. |
| Approach: | They propose to use label noise to imitate human errors in annotations . they use a noisy label noise benchmark to evaluate their methods . |
| Outcome: | The proposed benchmarks are different from data with heterogeneous label noises in the real world. |
Hide and Seek in Noise Labels: Noise-Robust Collaborative Active Learning with LLMs-Powered Assistance (2024.acl-long)
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| Challenge: | Existing methods for learning from noisy labels are difficult to improve . existing methods identify noisy labels and use active learning to query experts . |
| Approach: | They propose a collaborative learning framework to combine LLMs and small models for learning from noisy labels. |
| Outcome: | The proposed framework outperforms state-of-the-art baselines on synthetic and real-world noise datasets. |
Learning on Imbalanced Noisy Data via Debiased Sample Selection and LLM-Driven Annotation (2026.findings-acl)
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| Challenge: | Existing approaches to learning with noisy labels are prone to selection bias and training bias . obtaining large-scale high-quality datasets is expensive and time-consuming in practical scenarios . |
| Approach: | They propose an imbalanced learning with noisy labels task to let model learn from noisy labels . they first conduct debiased sample selection to better separate clean samples from noisy samples . then they feed selected clean samples to active annotator large language models for re-annotating noisy samples. |
| Outcome: | The proposed method is superior to existing methods on synthetic and real-world datasets. |