Hide and Seek in Noise Labels: Noise-Robust Collaborative Active Learning with LLM-Powered Assistance

• URL: http://arxiv.org/abs/2504.02901v1
• Date: Thu, 03 Apr 2025 04:36:39 GMT
• Title: Hide and Seek in Noise Labels: Noise-Robust Collaborative Active Learning with LLM-Powered Assistance
• Authors: Bo Yuan, Yulin Chen, Yin Zhang, Wei Jiang,
• Abstract summary: Learning from noisy labels (LNL) is a challenge that arises in many real-world scenarios where collected training data can contain incorrect or corrupted labels.<n>Most existing solutions identify noisy labels and adopt active learning to query human experts on them for denoising.<n>In this paper, we propose an innovative collaborative learning framework NoiseAL based on active learning to combine large language models with small models for learning from noisy labels.
• Score: 17.359530437698723
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Learning from noisy labels (LNL) is a challenge that arises in many real-world scenarios where collected training data can contain incorrect or corrupted labels. Most existing solutions identify noisy labels and adopt active learning to query human experts on them for denoising. In the era of large language models (LLMs), although we can reduce the human effort to improve these methods, their performances are still subject to accurately separating the clean and noisy samples from noisy data. In this paper, we propose an innovative collaborative learning framework NoiseAL based on active learning to combine LLMs and small models (SMs) for learning from noisy labels. During collaborative training, we first adopt two SMs to form a co-prediction network and propose a dynamic-enhanced threshold strategy to divide the noisy data into different subsets, then select the clean and noisy samples from these subsets to feed the active annotator LLMs to rectify noisy samples. Finally, we employ different optimization objectives to conquer subsets with different degrees of label noises. Extensive experiments on synthetic and real-world noise datasets further demonstrate the superiority of our framework over state-of-the-art baselines.

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