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

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

Large language models can perform well on general natural language tasks, but their
effectiveness is still not optimal for information extraction. Recent works indicate that the …

Large language models (LLMs) usually fall short on information extraction (IE) tasks and
struggle to follow the complex instructions of IE tasks. This primarily arises from LLMs not …

In this paper, we propose KnowCoder, a Large Language Model (LLM) to conduct Universal
Information Extraction (UIE) via code generation. KnowCoder aims to develop a kind of …

Recent work has shown that fine-tuning large language models (LLMs) on large-scale
instruction-following datasets substantially improves their performance on a wide range of …

Information Extraction (IE) is an essential task in Natural Language Processing. Traditional
methods have relied on coarse-grained extraction with simple instructions. However, with …

We introduce a new open information extraction (OIE) benchmark for pre-trained language
models (LM). Recent studies have demonstrated that pre-trained LMs, such as BERT and …

Large language models (LLMs) have demonstrated promising in-context learning
capabilities, especially with instructive prompts. However, recent studies have shown that …

Information extraction (IE) is a fundamental area in natural language processing where
prompting large language models (LLMs), even with in-context examples, cannot defeat …