Abstract Large Language Models (LLMs) have achieved unprecedented breakthroughs in
various natural language processing domains. However, the enigmatic``black-box''nature of …

Adopting a two-stage paradigm of pretraining followed by fine-tuning, Pretrained Language
Models (PLMs) have achieved substantial advancements in the field of natural language …

Classification models categorize objects into given classes, guided by training samples with
input features and labels. In practice, however, labels can be corrupted by human error or …

Commonsense reasoning based on knowledge graphs (KGs) is a challenging task that
requires predicting complex questions over the described textual contexts and relevant …

The creation of large-scale datasets annotated by humans inevitably introduces noisy
labels, leading to reduced generalization in deep-learning models. Sample selection-based …

With the emergence of foundation models, deep learning-based object detectors have
shown practical usability in closed set scenarios. However, for real-world tasks, object …

Annotating the dataset with high-quality labels is crucial for deep networks' performance, but
in real-world scenarios, the labels are often contaminated by noise. To address this, some …

While collecting training data, even with the manual verification of experts from
crowdsourcing platforms, eliminating incorrect annotations (noisy labels) completely is …

Learning with noisy labels has been studied to address incorrect label annotations in real-
world applications. In this paper, we present ChiMera, a two-stage learning-from-noisy …

Conditional diffusion models have shown remarkable performance in various generative
tasks, but training them requires large-scale datasets that often contain noise in conditional …