Within the vast expanse of computerized language processing, a revolutionary entity known
as Large Language Models (LLMs) has emerged, wielding immense power in its capacity to …

Abstract Low-Rank Adaptation (LoRA), which updates the dense neural network layers with
pluggable low-rank matrices, is one of the best performed parameter efficient fine-tuning …

Although large language models (LLMs) has shown great performance on natural language
processing (NLP) in the financial domain, there are no publicly available financial tailtored …

Large language models (LLMs) have demonstrated notable potential in conducting complex
tasks and are increasingly utilized in various financial applications. However, high-quality …

Temporal data, notably time series and spatio-temporal data, are prevalent in real-world
applications. They capture dynamic system measurements and are produced in vast …

Large language models (LLMs) have advanced financial applications, yet they often lack
sufficient financial knowledge and struggle with tasks involving multi-modal inputs like tables …

Large models have emerged as the most recent groundbreaking achievements in artificial
intelligence, and particularly machine learning. However, when it comes to graphs, large …

The recent trend of large language models (LLMs) is to increase the scale of both model
size (\aka the number of parameters) and dataset to achieve better generative ability, which …

Although large language models (LLMs) have shown great performance in natural language
processing (NLP) in the financial domain, there are no publicly available financially tailored …

eXtensible Business Reporting Language (XBRL) has attained the status of the global de
facto standard for business reporting. However, its complexity poses significant barriers to …