This paper presents a comprehensive survey of ChatGPT-related (GPT-3.5 and GPT-4)
research, state-of-the-art large language models (LLM) from the GPT series, and their …

This paper presents an exhaustive quantitative and qualitative evaluation of Large
Language Models (LLMs) for Knowledge Graph (KG) construction and reasoning. We …

As the significant subtask of information extraction, relation extraction (RE) aims to identify
and classify semantic relations between pairs of entities and is widely adopted as the …

Large language models (LLMs) have shown impressive abilities for open-domain NLP
tasks. However, LLMs are sometimes too footloose for natural language understanding …

Joint entity and relation extraction aims to extract relation triplets from plain text directly. Prior
work leverages Sequence-to-Sequence (Seq2Seq) models for triplet sequence generation …

Abstract Large Language Models (LLMs) demonstrate remarkable potential across various
domains; however, they exhibit a significant performance gap in Information Extraction (IE) …

Construction hazards occur at any time in outfield test sites and frequently result from
improper interactions between objects. The majority of casualties might be avoided by …

This paper presents an empirical study to build relation extraction systems in low-resource
settings. Based upon recent pre-trained language models, we comprehensively investigate …

With the technological development of entity extraction, relationship extraction, knowledge
reasoning, and entity linking, the research on knowledge graph has been carried out in full …

Existing research on large language models (LLMs) shows that they can solve information
extraction tasks through multi-step planning. However, their extraction behavior on complex …