Abstract: The problem of learning from long-tailed noisy data, referred to as Long-Tailed Noisy Label Learning (LTNLL), presents significant challenges in deep learning. LTNLL datasets are typically affected by two primary issues: class imbalance and label noise. While previous methods have addressed these problems separately, the simultaneous presence of both in real-world applications remains underexplored. In this paper, we introduce a simple yet effective method, **I**nstances **B**enefitting **C**lasses (**IBC**). Our philosophy is to simultaneously overcome overfitting to noisy classes and transfer knowledge between semantically related classes. At the instance level, we propose selecting top-$k$ semantically similar classes and use them to construct soft labels. Specifically, we soften noisy hard labels by reducing the probability of noisy classes and reallocating this probability to the semantically similar classes. **This reduces the model's overconfidence in noisy classes while enhancing its focus on tail classes.** We next propose a novel shot-specific multi-expert ensemble learning framework to make knowledge transfer more targeted, where we maintain multiple shot-specific soft labels for each instance, with each expert supervised by one of these labels. By integrating these experts, we demonstrate that IBC exhibits more separable representations, improving both overall and partition performance. Extensive experiments show that IBC outperforms existing state-of-the-art (SOTA) methods on a variety of benchmark and real-world datasets, achieving improvements ranging from **1.89\%** to **4.99\%** on the CIFAR-10 and CIFAR-100 datasets across all settings. **The source code is provided in the supplementary material.**