噪声标签中的捉迷藏：基于LLM辅助的噪声鲁棒协作主动学习
Hide and Seek in Noise Labels: Noise-Robust Collaborative Active Learning with LLM-Powered Assistance
AI总结：
提出基于主动学习的协作框架NoiseAL，结合大模型与小模型，通过动态阈值策略划分子集并利用LLM纠正噪声标签，有效提升噪声标签学习的鲁棒性。
摘要：
从噪声标签中学习（LNL）是许多现实场景中面临的挑战，这些场景中收集的训练数据可能包含错误或损坏的标签。大多数现有解决方案识别噪声标签并采用主动学习向人类专家查询以进行去噪。在大语言模型（LLM）时代，尽管我们可以减少人力来改进这些方法，但其性能仍受限于从噪声数据中准确分离干净样本和噪声样本。在本文中，我们提出了一种基于主动学习的创新协作学习框架NoiseAL，结合LLM和小模型（SM）来从噪声标签中学习。在协作训练期间，我们首先采用两个SM构成协同预测网络，并提出动态增强阈值策略将噪声数据划分为不同子集，然后从这些子集中选择干净样本和噪声样本，输入主动标注器LLM以纠正噪声样本。最后，我们采用不同的优化目标来克服具有不同程度标签噪声的子集。在合成和真实世界噪声数据集上的大量实验进一步证明了我们的框架相对于最先进基线的优越性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
