This survey addresses the crucial issue of factuality in Large Language Models (LLMs). As
LLMs find applications across diverse domains, the reliability and accuracy of their outputs …

The utilization of large language models (LLMs) in the Healthcare domain has generated
both excitement and concern due to their ability to effectively respond to freetext queries with …

Retrieval augmentation can aid language models (LMs) in knowledge-intensive tasks by
supplying them with external information. Prior works on retrieval augmentation usually …

Advancements in LLMs have recently unveiled challenges tied to computational efficiency
and continual scalability due to their requirements of huge parameters, making the …

Large-scale pre-trained models (PTMs) have been widely used in document-oriented NLP
tasks, such as question answering. However, the encoding-task coupling requirement …

Language models deployed in the wild make errors. However, simply updating the model
with the corrected error instances causes catastrophic forgetting--the updated model makes …

Pre-trained language models (PLMs) have achieved remarkable results on NLP tasks but at
the expense of huge parameter sizes and the consequent computational costs. In this paper …

Dependency trees reflect rich structural information, which can effectively guide models to
understand text semantics and are widely used for relation extraction. However, existing …

Large language models (LLMs) are proficient in capturing factual knowledge across various
domains. However, refining their capabilities on previously seen knowledge or integrating …

Verbal humor is commonly mentioned to be a complex phenomenon that requires deep
linguistic and extralinguistic forms of knowledge. However, state-of-the-art deep learning …