作者位次 #2/3ACL 2026Findings· Findings of ACL
Learning on Imbalanced Noisy Data via Debiased Sample Selection and LLM -Driven Annotation
Bo Yuan · Yulin Chen · Yin Zhang
摘要
Learning with Noisy Labels (LNL) is a challenge where the collected training set can contain incorrect or corrupted labels. Most existing solutions distinguish clean samples from noisy samples and query human experts on noisy samples for denoising. However, these solutions often operate under the...