Abstract Information Extraction (IE) aims to extract structural knowledge from plain natural
language texts. Recently, generative Large Language Models (LLMs) have demonstrated …

Relation extraction (RE) involves identifying the relations between entities from underlying
content. RE serves as the foundation for many natural language processing (NLP) and …

Manually integrating data of diverse formats and languages is vital to many artificial
intelligence applications. However, the task itself remains challenging and time-consuming …

Large language models (LLMs) create a new paradigm for natural language processing.
Despite their advancement, LLM-based methods still lag behind traditional approaches in …

Massive amount of unstructured text data are generated daily, ranging from news articles to
scientific papers. How to mine structured knowledge from the text data remains a crucial …

This work addresses the challenge of limited real-world speech data in speech de-
identification, the process of removing Personally Identifiable Information (PII). We formulate …

Relation extraction (RE), a crucial task in NLP, aims to identify semantic relationships
between entities mentioned in texts. Despite significant advancements in this field, existing …

\textbf {Objective:} We aimed to develop an advanced multi-task large language model
(LLM) framework to extract multiple types of information about dietary supplements (DS) …

Knowledge graphs (KGs) generated by large language models (LLMs) are becoming
increasingly valuable for Retrieval-Augmented Generation (RAG) applications that require …

Despite widespread applications of knowledge graphs (KGs) in various tasks such as
question answering and intelligent conversational systems, existing KGs face two major …