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 …

Extracting structured knowledge from scientific text remains a challenging task for machine
learning models. Here, we present a simple approach to joint named entity recognition and …

There are enormous enthusiasm and concerns in applying large language models (LLMs) to
healthcare. Yet current assumptions are based on general-purpose LLMs such as ChatGPT …

Pre-trained language models have attracted increasing attention in the biomedical domain,
inspired by their great success in the general natural language domain. Among the two main …

Relation extraction (RE) is the core NLP task of inferring semantic relationships between
entities from text. Standard supervised RE techniques entail training modules to tag tokens …

The strong few-shot in-context learning capability of large pre-trained language models
(PLMs) such as GPT-3 is highly appealing for application domains such as biomedicine …

Computer-aided process planning is the bridge between computer-aided design and
computer-aided manufacturing. With the advent of the intelligent manufacturing era, process …

Large language models (LLMs) have great potential for synthetic data generation. This work
shows that useful data can be synthetically generated even for tasks that cannot be solved …

Generative Knowledge Graph Construction (KGC) refers to those methods that leverage the
sequence-to-sequence framework for building knowledge graphs, which is flexible and can …