Papers with synthetic and real-world noise datasets

1 papers
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.

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