Language models (LMs) have demonstrated remarkable capabilities across a wide range of
tasks in various domains. Despite their impressive performance, the reliability of their output …

As a main field of artificial intelligence, natural language processing (NLP) has achieved
remarkable success via deep neural networks. Plenty of NLP tasks have been addressed in …

Although Large Language Models (LLMs) have shown great potential in Natural Language
Generation, it is still challenging to characterize the uncertainty of model generations, ie …

Many text classification tasks are inherently ambiguous, which results in automatic systems
having a high risk of making mistakes, in spite of using advanced machine learning models …

Abstract Large Language Models (LLMs) show promising results in language generation
and instruction following but frequently “hallucinate”, making their outputs less reliable …

Recent advancements in the capabilities of large language models (LLMs) have paved the
way for a myriad of groundbreaking applications in various fields. However, a significant …

Uncertainty estimation is crucial for the reliability of safety-critical human and artificial
intelligence (AI) interaction systems, particularly in the domain of healthcare engineering …

Recently, aspect sentiment quad prediction has received widespread attention in the field of
aspect-based sentiment analysis. Existing studies extract quadruplets via pre-trained …

Large language models (LLMs) have demonstrated remarkable capabilities across a wide
range of tasks in various domains. Despite their impressive performance, they can be …

This article presents a comprehensive survey on test optimization in deep neural network
(DNN) testing. Here, test optimization refers to testing with low data labeling effort. We …