Thepre-train, prompt, predict'paradigm of large language models (LLMs) has achieved
remarkable success in open-domain question answering (OD-QA). However, few works …

We study fair machine learning (ML) under predictive uncertainty to enable reliable and
trustworthy decision-making. The seminal work of'equalized coverage'proposed an …

The efficacy of supervised deep learning in medical image analyses, particularly in
pathology, is hindered by the necessity for extensive manual annotations. Annotating …

Over the past decades, text classification underwent remarkable evolution across diverse
domains. Despite these advancements, most existing model-centric methods in text …

Scientific Natural Language Inference (NLI) is the task of predicting the semantic relation
between a pair of sentences extracted from research articles. The automatic annotation …

In this paper, we introduce SemiRES, a semi-supervised framework that effectively
leverages a combination of labeled and unlabeled data to perform RES. A significant hurdle …

In e-commerce, accurately extracting product attribute values from multimodal data is crucial
for improving user experience and operational efficiency of retailers. However, previous …

Large Language Models (LLMs) have achieved unparalleled success across diverse
language modeling tasks in recent years. However, this progress has also intensified ethical …

Weakly supervised text classification leverages only label class names as signals to train
classifiers. Most existing methods rely on various techniques such as keyword-driven or …

Abstract Natural Language Inference (NLI) aims at recognizing the semantic relationship
between a pair of sentences---whether one sentence entails the other sentence, contradicts …